Differential scanning calorimetry in the biopharmaceutical sciences

C H A P T E R

11 Differential scanning calorimetry in the biopharmaceutical sciences Stephen J. Demaresta, Verna Frascab a

Eli Lilly Biotechnology Center, San Diego, CA, United States; bField Applications Manager, Malvern Panalytical, Northampton, MA, United States

11.1 Background Proteins are polypeptides that condense (or fold) in aqueous solution to form compact, ordered, and highly complex structures in solution. The primary amino acid sequence of a protein determines the compact structure it can adopt and its mechanism for folding to this state [1]. The folded structures of proteins impart their ability to perform various biological tasks such as signaling, scaffolding, transporting of various ions or metabolites, or making or breaking covalent bonds (enzymes). Most therapeutic proteins require being in a folded state to perform their desired function (Fig. 11.1). Maintenance of the folded state is key to manufacturability and durability of protein therapeutics in both liquid or dried solid formulations [2]. Differential scanning calorimetry (DSC) provides unique insights into the stability of these folded conformations and can be used to identify or engineer molecules with optimal stability profiles and in the development of robust handling procedures and long-term storage conditions. In solution, high pressure, elevated temperatures, extremes of pH, or exposure to chemical denaturants (e.g., guanidinium hydrochloride or urea), nonpolar solvents, or nonaqueous interfaces such as air/water interfaces can denature a protein’s folded conformation leading to an expanded unfolded state with a higher degree of freedom. A two-state approximation has been used to describe the denaturation of proteins where only a folded (or native) state, denoted F or N, and an unfolded (or denatured) state, denoted U or D exist. This two-state unfolding view evolved over time to describe these individual states as dynamic and representing a continuum of properties that each change gradually with these extrinsic conditions [3]. Generally, the folded stability of proteins is marginal having free energies of unfolding between 5 and 20 kcal/mol at 25
C [4e6]. The marginal stability of proteins near physiological

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FIG. 11.1

Ribbon diagrams tracing the backbones of various pharmaceutical proteins that can be studied by DSC. (A) Insulin is a peptide that forms a hexameric complex coordinated around a Zn2þ ion (depicted as a sphere at the center) and is used to augment native insulin in diabetic patients. (B) Interferon-b is a single domain protein used for treating patients with multiple sclerosis. (C) Immunoglobulins (antibodies) of the gamma subtype (IgG) represent the largest class of biologic drug moieties currently in the clinic as they can be developed to target virtually any extracellular or membrane antigen with exquisite specificity. IgGs are heterotetrameric multidomain proteins consisting of two heavy chains each with a variable domain and three constant domains (VH, CH1, CH2, and CH3) and two light chains each with a variable domain and a single constant domain (VL and CL). The multidomain nature of antibodies gives rise to multiple unfolding transitions in their heat capacity profiles as shown in Figs. 11.3e11.5. The protein database (pdb) codes for each of these structures is shown next to each diagram.

temperatures is the result of two dominant forces, the hydrophobic effect and configurational entropy, that support and oppose protein folding, respectively [5]. The hydrophobic effect relates to how proteins sequester their nonpolar sidechains (e.g., leucine, isoleucine, phenylalanine, tryptophan, etc.) from water in a process that thermodynamically mimics the transfer of nonpolar solutes into aqueous solvents [7]. The burial of hydrophobic groups is highly

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stabilizing for proteins. Like the transfer of nonpolar solutes into aqueous solutions, there is a large heat capacity difference (DCP) between the folded and unfolded states of proteins. This DCP results in the strong temperature-dependence of protein stability. Proteins can be denatured at high temperatures (most commonly) and at very low temperatures. In the latter case the phenomenon is known as cold denaturation [8]. For most proteins, cold denaturation would occur at temperatures significantly below freezing; however, it can be induced at temperatures near or above freezing for some proteins under nonoptimal pH or denaturant conditions [9,10]. The dominant force opposing the protein folding, configurational entropy, relates to the number of degrees of freedom a macromolecule has access to. The overall configurational entropy of proteins is influenced by both local and nonlocal (excluded volume) effects [5]. For folded proteins, the adoption of a compact, glass-like native state highly restricts the degrees of freedom its polypeptide chain has access to. The net effect of combining the nearly balanced energetic effects from the stabilizing hydrophobic burial and the destabilizing decrease in configurational entropy results in the relatively marginal stability of proteins. Along with the burial of hydrophobic groups, folded proteins can bury the polar groups. To offset the resulting positive effects of hydrogen binding with solvent, proteins generally maintain exquisite and complex networks of hydrogen bonds, which drives the secondary structures (e.g., a-helices, b-sheets, turns, etc.) they adopt [11]. Balancing of chargeecharge interactions on their surfaces and maximizing van der Waals contacts within their cores further contributes to a protein’s overall net thermodynamic stability. The use of the two-state approximation enables fundamental thermodynamic parameters to be ascribed to protein stability [12]. The two-state approximation can be applied to individual protein domains that unfold/refold in a fully reversible process. Originally, spectroscopic methods were used to determine the fractions of folded and unfolded protein at any given temperature. Using the two-state approximation, the free energy of unfolding, DG(T) (i.e., the stability), can be determined using a modified form of the GibbseHelmholtz equation where DCP is considered constant and the entropy term is factored out using the midpoint of the thermal unfolding transition, Tm, as the reference temperature:      T T DGðTÞ ¼ DHm 1  þ DCP T  Tm  T ln Tm Tm

(11.1)

A common method to determine the value of DCP for protein unfolding is to use the equilibrium constant (derived from the free energy) to determine the van’t Hoff enthalpy: ln KU ¼ 

DHVH DS þ R RT

(11.2)

where KU is the equilibrium constant, DHVH is the van’t Hoff enthalpy, and DS is the change in entropy from folded to unfolded. The van’t Hoff equation is the derivative of Eq. (11.2), but Eq. (11.2) can be used to obtain the van’t Hoff enthalpy by plotting ln KU versus the reciprocal of the temperature where the slope is (DH/R) and the intercept is DS/R. The Kirchoff equation is then used to calculate DCP, dDH=dT ¼ DCP

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(11.3)

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however, there can be a significant level of error in the measurement. To more accurately determine DCP, the van’t Hoff enthalpy can be measured at various pH values or denaturant (urea or guanidinium HCl) concentrations to obtain an average [6,13]. The magnitude of DCP has been shown to be proportional to the amount of surface area that a folded protein domain buries, which is roughly proportional to its size [14]. Now, DSC has become one of the main methods (if not the main method) for both quantitatively and qualitatively determining the stability of a protein’s folded state. DSC was first adapted to the study of protein denaturation in the 1960s [15e18] and provided unique insights into the fundamental thermodynamics of protein denaturation and protein stability [6,7]. A clear advantage exists for DSC in the measurement of multiple thermal transitions, which are often the result of the unfolding of multiple, independently folded domains within proteins. This is due to the derivative nature of DSC data. DSC measures the heat capacity of a system by determining the heat input required to change the temperature of the system (CP ¼ dH/dT). DSC is used to directly measure the enthalpy of unfolding DHU for one or more folded protein domains. DSC can also be used to directly measure DCP between the folded and unfolded states of the protein. A plot of the thermal unfolding of a single-domain protein by DSC is shown in Fig. 11.2(A) based on a study published by Jackson and Brandts [17]. The heat capacity of the folded state is shown in the segment from A to B while that of the unfolded state is shown in segment C to D. DCP between segments B and C represents the combination of DCP based on the changing populations of the folded and unfolded states as well as the excess DCP required to denature the protein. The point M (known as Tm) on the curve represents the temperature where 50% of the protein is folded and 50% is unfolded. To measure the calorimetric enthalpy (DHcal) of the unfolding reaction, the area under the unfolding transition is integrated. An obvious issue regarding the integration of DSC-derived unfolding data is where to draw the baseline under the transition. In Fig. 11.2(A), Jackson and Brandts [17] extrapolated the folded heat capacity (baseline prior to unfolding) to point M to form the first half of the baseline and extrapolated the unfolded heat capacity (baseline after unfolding) to point M to form the second half of the baseline. Other methods, described in Section 11.4 (below), can also be used to obtain a baseline for integration. DCP between the folded and unfolded states is determined by the height of the segments PQ in Fig. 11.2A. Integration of the experimental heat capacity, CP, yields a sigmoidal curve that can be used to estimate the average enthalpy over the temperature range of the transition, which is equivalent to the mole fractions of the folded (1  x) and unfolded states (x), Havg ¼ xHU þ (1  x)HF (Fig. 11.2B). If a two-state unfolding approximation can be made, the unfolding equilibrium constant, KU(T), can be determined based on the mole fractions. The van’t Hoff equation can then be used to generate the other thermodynamic parameters [17]. To determine KU(T) at temperatures significantly below the transition (i.e., for therapeutic proteins it is useful to know KU at 4
C, 25
C and 37
C), one must assume that DCP is constant within the temperature range of interest. However, the CP of unfolded states and folded states have temperature dependencies of their own that are not equal to each other (see the different slopes of the folded and unfolded CP baselines in Fig. 11.2A); therefore, the further from the transition where DCP is measured, the less secure the constant DCP assumption becomes and the less accurately KU(T) can be estimated. Unfortunately, most therapeutic proteins do not thermally unfold through a reversible two-state unfolding mechanism. This limits the thermodynamic interpretation of their

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11.1 Background

(A)

M

CP P B

N

A CP

(B)

C

Q T

TL

D D CP

TH

HD H

HN TEMPERATURE

FIG. 11.2 Temperature studies. (A) Schematic illustration of the temperature-dependence of the heat capacity in the temperature region where the thermal transition occurs. (B) Variation in the enthalpy with temperature in the region of the thermal transition, obtained by integration of the curve in part (A). Extrapolated base lines are shown as dashed lines. Reprinted with permission from Jackson WM, Brandts JF. Thermodynamics of protein denaturation. A calorimetric study of the reversible denaturation of chymotrypsinogen and conclusions regarding the accuracy of the two-state approximation. Biochemistry 1970;9:2294e2301.

thermal unfolding profiles. Complex proteins can unfold through one or more intermediate partially folded states on the path to a fully unfolded state. More commonly, proteins deviate from a two-state unfolding approximation because they unfold irreversibly during or subsequent to denaturation. Unlike protein unfolding in solubilizing denaturants like urea or guanidinium hydrochloride, temperature-induced unfolding leads to the exposure of hydrophobic groups that can result in insolubility or irreversible aggregation. Irreversibility can be measured by cooling the unfolded protein to a temperature where it should refold, then heating the solution again and observing the magnitude of the second unfolding transition (if there is one). Even without performing a refolding experiment, protein aggregation and precipitation can often be identified by certain traits within a DSC thermogram (described below in Section 11.4). Another test of the two-state approximation is whether DHVH matches DHcal. If the two parameters are not equal, then the data cannot be analyzed by the simple two-state approximation. Measurements of DHVH > DHcal are typically indicative of protein oligomerization. The ratio of DHVH/DHcal can provide information regarding the extent of oligomerization [19e21]. Alternately, unfolding transitions with

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DHVH < DHcal likely involve intermediate or partially unfolded protein states [20]. Experimental exercises to further evaluate oligomerization, aggregation, and precipitation are described below in Section 11.4. More recently solution DSC has been utilized to evaluate kinetic barriers to folding and unfolding [22], mechanisms and kinetics of amyloid formation [23e26], thermodynamic contributions to small molecule drug design [27], and even the nature of complex solutions like plasma or urine to evaluate hallmarks of disease [28]. While this chapter will focus primarily on the use of DSC to study the biophysical properties of dilute protein solutions, DSC is also one of the few techniques including FTIR that can be used to characterize frozen protein solutions and dried protein solids. Upon freezing at low temperatures (below the glass transition described below), protein solutions are generally composed of a mixture of crystalline solvent and an amorphous phase that contains the protein, solutes, and amorphous noncrystallized water molecules where translations and rotations are highly limited [29]. Transitions that occur by heating this semicrystalline state include the glass transition (denoted T0 g), which is connected to or rapidly followed by the micro- and macrocollapse transitions (i.e., breakdown of the porous amorphous state at a temperature called “Tc”), full crystallization, and melting. In frozen solutions, T0 g generally occurs at temperatures well below freezing. In dried protein formulations, the glass transition (denoted Tg) is significantly elevated. In practice, thermal denaturation of a protein’s folded structure in the solid state by DSC occurs at very high- and very low temperatures relative to the Tm measured for protein solutions and is not the stability-determining variable. The Tg (or T0 g) of protein solids is generally the lowest temperature transition for frozen or dried protein solids. The glass transition is seen as a shift in the DSC thermogram baseline to higher heat capacity and is associated with a “higher state of mobility” that includes whole molecule rotations, vibrations, and translations, which if exceeded during freeze-drying can lead to collapse of the porous amorphous structure of a frozen solid or ordered crystallization [30]. Movements within the solid at temperatures above the Tg can enable chemical decomposition, aggregation, or physical denaturation of the protein. Therefore, T0 g and Tg are generally used to predict the freeze-dry behavior and long-term stability of solid protein formulations, respectively. The Tg can be reported as the midpoint of the glass transition or the onset temperature (the start of the transition). The Tg of a freeze-dried (lyophilized) protein is commonly depressed when water adsorbs to the powder, which can have an impact on a dried protein’s stored stability. Many folded proteins retain their native states during and after freeze-drying, which reduces aggregation and chemical modifications [31]. Glass transition temperatures of frozen protein solutions are typically in the range of 10 to 15
C; therefore, it is practical to freeze-dry proteins at temperatures between 35 and 40
C, well below T0 g. Glass transitions of freeze-dried proteins can be difficult to observe and are characterized by a broad and weak (i.e., small DCP) transition in the thermogram [30]. T0 g values are sensitive and often depressed (i.e., occur at lower temperature) as the molar ratio of stabilizer (often sugars) in a freezedried protein sample increases [32]. Thus, there is a careful balance between adding stabilizers that increase the conformational stability of a protein and the depression of the T0 g to near the freeze-dry temperature. Overall, it is clear that DSC of dilute protein solutions versus solid protein formulations is very different; however both solution and solid DSC are valuable tools for the characterization and development of protein therapeutics.

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11.2 DSC instruments DSC instruments used for solids are a different design than DSC instruments used for dilute protein solutions. Therefore, the discussion of the two types of instruments is separated into two sections.

11.2.1 Solution DSC instrumentsdmicrocalorimeters Solution DSC instruments are designed to be highly sensitive and to handle liquid heating in ways challenging to the DSC instruments designed for use with solids. Solution DSC instruments are generally denoted “microcalorimeters”. There are two different microcalorimeter designs that are common in the industry, which both utilize power compensated heating of a sample and a reference cell. The first are calorimeters with coin-shaped or cylindrical cells that measure differences in the energy required to heat a sample cell and a reference cell to determine changes in CPdoften referred to as feedback. The second class of microcalorimeters has tightly coiled capillary cells that advantageously require less volume and protein material. Both Malvern Panalytical (MicroCal) and TA Instruments manufactured both classes of scanning microcalorimeters. The Malvern Panalytical capillary DSCs (MicroCal VP-Capillary DSC and MicroCal PEAQ-DSC) and TA Instruments capillary DSC (Nano DSC) can be purchased with automation to enable extensive cleaning between protein scans and automated samplingdmaking the instruments highly “hands-off”, which has significantly expanded the utility of DSC by eliminating these time-consuming steps. The coin-shaped or cylindrical cells enable the ability to examine larger volumes and can be more sensitive than capillary DSC instruments. Perhaps, the most important difference between the capillary and noncapillary instruments is how they react to aggregation/ precipitation (Fig. 11.3A). When proteins (particularly antibodies) aggregate and precipitate during unfolding transitions in the coin/cylindrical cells, there is generally a sharp and negative change in CP associated presumably with the loss of protein from the dilute solution as the protein settles to the bottom of the cell. This can negate the ability to observe multiple unfolding transitions within complex multidomain proteins like antibodies. This phenomenon is not as prevalent in the capillary microcalorimeters as it is in the standard solution microcalorimeter designs, although it can and does occur [33]. Therefore, with a capillary design microcalorimeter, a user is more likely to observe the higher temperature transitions within complex proteins that may be important for their design or characterization.

11.2.2 DSC instruments for solids DSC instruments used for solids typically utilize sample pans placed on a heated surface for analysis. Heating and thermal measurements can be performed using dual-furnace power compensation (PerkinElmer, others) like the microcalorimeters or through heat flux, which uses a single furnace (an IR source) to heat both the reference and sample pans (this includes the high-sensitivity Discovery series DSCs from TA instruments and PerkinElmer 4000/6000 series). In a heat-flux DSC instrument, sample protein (in a sealed pan) and the empty reference pan are placed on the same furnace, which is heated at a linear heating rate

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FIG. 11.3 Heat capacity profiles of a human IgG1 monoclonal antibody in phosphate-buffered saline (pH 7) at varying scan rates. (A) Heat capacity diagrams of the protein at the shown scan rates prior to baseline corrections. A change in the baseline toward lower heat capacity subsequent to the thermal unfolding transitions of all the IgG1 domains is a hallmark of aggregation or precipitation and occurs more prevalently at slower scan rates as this is generally a kinetically irreversible step. (B) Heat capacity diagrams of human IgG at various scan rates subsequent to baseline correction. There is an increase in the apparent measured midpoints of thermal denaturation (Tm) values as temperature is increased. DSC scans were performed with 1 mg/mL protein in PBS using the low feedback mode. The Y-axis in (A) was converted to kcal/mol/
C from mCal/s based on the individual protein concentrations and the scan rate.

(typically 5e10
C/min), and heat is transferred to the pans through a thermoelectric disk. The heat flow required to heat the different cells is then determined as the temperature changes during the temperature scanning process. This heat flow versus temperature is then plotted to generate the resulting thermogram. In a power-compensated DSC instrument, the sample and reference pans are placed on separate furnaces. During heating, both pans are maintained at the same temperature as the temperature changes during the temperature

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scanning process. The instrument then measures the power needed to keep the temperatures the same as a function of temperature to generate the resulting thermogram, which is again a plot of heat flow versus temperature. There are additional features (usually associated with high-end instruments) that can improve signal-to-noise and data interpretation. Some manufacturers (such as PerkinElmer or Mettler Toledo) offer “high ramp rate” or “high heating rate” DSC instruments (abbreviated as HRR-DSC). With high ramp rates, transitions occur more quickly, which allows for a greater sensitivity to the heat capacity changes as the amplitudes are measured in a shorter period of time. This technique can enable the observation of very weak thermal transitionsdsuch as those associated with Tg. It should be noted that many DSC instruments not specifically denoted HRR-DSCs (like the TA Discovery series DSCs) offer high ramp rates near those of the HRRs. Under these circumstances, the baseline stability of the instrument is often a more critical factor for observing weak thermal transitions. Another attractive instrument feature (offered by TA-instruments and PerkinElmer) for measuring solid transitions is called “temperature-modulated” DSC (abbreviated as TM-DSC or MT-DSC). Temperature modulation allows for the differentiation of “reversible” and “nonreversible” transitions. This feature can be critical for deconvoluting overlapping reversing and nonreversing transitions like Tg and evaporation, respectively.

11.3 Practical considerations for DSC use 11.3.1 Solution DSC operational considerations Very little training is required to use solution DSC instruments. Described here are some of the key operating parameters for obtaining reproducible and accurate data.

11.3.2 Sample handling There are many sample handling tips to keep in mind when generating DSC data; however, careful buffer consideration is certainly the most important parameter for data consistency. Temperature is not the only method for unfolding proteins; they are also highly sensitive to pH, ionic strength, buffer components, cofactors, and other solution parameters. If the experimental goal is a stability comparison of two separate lots of the same protein or a stability comparison of highly similar protein variants, careful and consistent buffer matching between samples is imperative for a data comparability. Even small changes in buffer composition can change the folded stability of a protein and make comparability between two protein samples impossible. As a result, dialysis or other methods of complete buffer matching with the solutions to be used in the reference cells can significantly improve baselines. For comparability studies, maintaining a uniform protein concentration (often 1 mg/mL) is important, particularly for nonreversible unfolding proteins like antibodies since aggregation is concentration-dependent and can impact the observable Tm and the temperature at which aggregation/precipitation induces a drop in heat capacity (Fig. 11.4A and B). Of course, comparability is only one use for DSC. The impact of aqueous conditions (pH, ionic strength, etc.) on the stability of proteins by carefully comparing different buffer variables on protein stability

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FIG. 11.4 Reproducibility of the concentration normalized heat capacity profiles of human IgG1 in PBS using a capillary DSC (MicroCal VP-CapillaryDSC, Malvern Panalytical). (A) subsequent to and (B) prior to baseline subtraction. DSC scans were performed at 90
C/h using the low feedback mode. The Y-axis in (A) was converted to kcal/mol/
C from mCal/s based on the individual protein concentrations and the scan rate.

is another common application of DSC, which can be used to determine the most favorable buffer conditions for protein formulation, handling, or processing (i.e., purification). Careful and consistent buffer matching for generating reliable baselines is no less important for these applications. Lastly, bubbles in the sample chamber can lead to strong mismatches between the sample and reference cells and negatively impact baseline performance. This is a particular issue for nonautomated coin or cylindrical cell instruments with manual loading. Bubbles are not as common a problem for the automated sample handlers provided the samples do not undergo a series of heating/cooling steps before being run that leads to gas absorption and enough volume is provided to avoid gas being drawn into the system along with the sample.

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11.3.3 Resolution/sensitivity Resolution is controlled by two parameters: the temperature ramp rate (which will be just referred to as “ramp rate”) and the feedback mode (i.e., how often the temperatures between the sample cell and reference cell are compared). Slowing the scan rate or increasing the feedback mode can improve resolution, but generally at the cost of signal-to-noise (see Fig. 11.5 for the impact of changes in feedback mode). Alternately, ramping too quickly can reduce the ability to resolve multiple transitions. Therefore, it is generally suggested that a compromise between ramp rate and feedback modes be used to maximize both signal-to-noise and resolution when first observing the transition profile of an unknown protein. For both the MicroCal (Malvern Panalytical) and TA Instruments DSCs, using a scan rate of 90
C/h in a moderate feedback mode (“Low” in the MicroCal instrument) is a good compromise. Once it is determined whether resolution or sensitivity is more critical for obtaining the best data for a sample, the user can determine whether to increase or reduce the scan rate or the feedback rate. For many reasons, scan rates can have a significant impact on the measured Tm values for protein samples, particularly those that unfold irreversibly (Fig. 11.3A and B). Therefore, it is recommended that once a scan rate is established, that it not be changed for a particular set of proteins, otherwise the data from day-to-day may not be comparable. The sensitivity of the both the TA Instruments and MicroCal (Malvern Panalytical) microcalorimeters are very high and enable reliable measurements of thermal transitions at protein concentrations ranging from 10 mg/mL down to approximately 0.2 mg/mL. This concentration range is highly protein-dependent. It is possible to generate interpretable data

FIG. 11.5 Baseline noise levels of the heat capacity profiles of a human IgG1 monoclonal antibody using low, medium, and high levels of feedback (denoted NONE, LOW, and MID on the MicroCal VP-Capillary DSC). Additional feedback can improve resolution (although it is not apparent in the full DSC curves shown in the inset), but also lowers the signal-to-noise of the experiment as shown by the increase in the noise level of the baseline. DSC scans were performed at 90
C/h using 1 mg/mL protein in PBS.

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at lower concentrations; however, performing reliable baseline subtractions at low concentrations can lead to error. Fig. 11.4B shows nonbaseline-subtracted thermal unfolding DSC curves for a monoclonal IgG1 antibody at varying concentrations. It is easy to understand how manual baseline subtractions may be challenging, particularly for the high-temperature unfolding peak corresponding to the CH3 domain of the antibody (Figs. 11.3A and 11.4B). Some proteins exhibit larger DHU than others and their thermal unfolding transition(s) can be discerned at concentrations below 0.2 mg/mL. Alternately, high concentrations (10 mg/mL and above) can lead to significant buildup of aggregated material in the capillary instruments or even clog them making their cleaning very difficult or in extreme cases, impossible. Therefore, the measurement of thermal transitions of ultrahigh concentration protein solutions (>10 mg/mL) using microcalorimetry is generally not recommended.

11.3.4 Assessing the performance and reliability Performance and reliability of solution DSC instruments can be evaluated in several ways. First, protein standards can be used with fixed-run parameters to demonstrate data comparability over time. Kits specifically designed for this purpose can be ordered that use RNase or Lysozyme. National Institute of Standards and Technology monoclonal antibody (NISTmAb) reference material is a representative IgG1 mAb [34,35], which could be used for this purpose. DSC analysis of NISTmAb results in three main thermal transitions, the first assigned as the CH2 domain, the second transition was assigned as the CH3 domain, while the third transition was assigned as the Fab domain [36]. Repeat Water/Water runs in the Sample/Reference cells are performed to demonstrate reproducible baselines. During long DSC runs with multiple samples, buffer/buffer scans can be compared throughout the experiment to determine the baseline stability of the instrument. The most common reliability issue arises when denatured protein slowly adsorbs to the walls of the instrument cells that act as catalysts for protein unfolding. If reference protein samples begin to drift toward lower Tm values over time, this is a clear indication that adsorbed protein is having an adverse impact on DSC data. Therefore, it is generally advised that the cells be cleaned with strong detergents like Contrad 70 or Decon 90 after every protein run to dissolve any adsorbed protein. If these issues continue, then it is time to discuss solutions with the manufacturer or service engineers.

11.3.5 Maintenance A low level of maintenance is generally required to keep a microcalorimeter working well. First, solvents used for washing heated samples from the instrument should contain a low level of bacteriostatic agent (e.g., 0.01% NaN3) to keep contamination at a minimum. Additionally, detergent buffers should be included in the wash protocols. Acidic detergents, such as Contrad 70 or Decon 90, work well. For automated instruments like the MicroCal VP-Capillary DSC and PEAQ-DSC (Malvern Panalytical), the autosampler uses a standard Hamilton syringe to draw and deposit samples, wash buffers (often water), and detergents. These syringes can become loose and their plungers require normal replacementdwe operate on a 3e6 month schedule, but instruments with inordinately high usage may need more

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frequent syringe/syringe plunger replacements. Regular maintenance by the manufacturer (at least once per year) has proven very important for maintaining reliable datadas they have cleaning protocols of their own and will routinely replace certain parts such as seals, valves, etc., that get worn or deformed through extended usage.

11.3.6 Solid-DSC operational considerations As this chapter is devoted primarily to the solution DSC, we will not discuss the practical considerations for the study of solid-state biopharmaceuticals in detail here. Solid DSC has many unique operational parameters, such as the sealing of sample pans to reduce volatile release and the control of moisture adsorption that can have a significant impact on the phase change parameters of dried solids. Also, empty pans are often used on the reference surface. Calibration of the instruments before use is often done using Indium as a standard [30,37], which displays a consistent melting onset temperature and heat of fusion. As described above, scan rates are typically much faster in solid DSC instruments than solution instruments to increase the sensitivity to small signal changes.

11.4 Data analysis 11.4.1 Solution DSC data analysis At first glance, interpretation of thermally induced protein unfolding seems very straightforward; however, data analysis can be more complex than many realize. For instance, the observation of the melting transitions within various samples is straightforward using DSC and comparison of the relative stabilities of very similar samples (i.e., those with minimal sequence and structural differences) is quite valid. However, as one compares proteins of different structure, the Tm becomes less important than the overall free-energy of unfolding extrapolated to temperatures of interest (4
C, 25
C, or 37
C). Proteins that bury large amounts of surface area in their folded states tend to have more steep transitions as determined by the DHVH, while those that bury less surface area tend to have broad transitions. If a two-state unfolding approximation can be made (see Section 11.1), the steeper transitions extrapolate to lower free energies at the lower temperatures. Under such circumstances, a protein with a low Tm, but very steep unfolding transition, can actually be more stable at low temperatures than a protein with a higher Tm, but with a shallow unfolding transition. Of course, the two-state approximation must be satisfied to enable a valid thermodynamic comparison. This requires that protein domains fold and unfold reversibly, which is often not the case with thermally denaturing complex therapeutic proteins. Additionally, it is important to recognize various features within DSC thermograms that have physical meaning other than strictly unfoldingdsuch as precipitation, native-state self-association, or denatured state aggregation. Precipitation is marked by a sharply negative drop in the DSC thermogram baseline that often occurs in the post-transition baseline (Fig. 11.3A and 11.4B). For self-associating systems, as protein concentration is increased, native state self-association (dimerization, trimerization, etc.) leads to an increase in Tm resulting from stabilization of the native state [38]. Denatured state oligomerization or aggregation decreases

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the Tm through stabilization of the denatured state at higher protein concentrations. Situations can arise where both the native and denatured (or intermediate) states are oligomeric and the concentration dependence of the Tm is complex. A hallmark of aggregation is a decline in the post-transition baseline (CP) as aggregates become more compact than fully unfolded polypeptides (Fig. 11.3A and 11.4B). There are increasingly complex models that can be applied to evaluate these more complicated transitions thermodynamically [39]; however, as unfolding becomes irreversible, these models become difficult to apply. Both Malvern Panalytical and TA Instruments provide analysis software with their instruments. MicroCal VP_Capillary DSC analysis package consists of algorithms embedded in OriginÒ, a scientific data analysis and graphics software. MicroCal PEAQDSC offers a stand - alone PEAQ-DSC software package. TA instruments also offers a standalone software package, NanoAnalyzeÔ. All programs have enabled multiple simultaneous processing of multiple thermograms within large and associated data sample sets generated using the automation of the instruments. All packages enable the subtraction of buffer backgrounds and baseline curvature and the ability to fit multiple transitions within complex DSC thermograms. Different forms of baseline corrections can be used for integrating DHcal. As mentioned in the background section, a step function was used in the early days of DSC to connect the baselines for the fully folded and fully unfolded segments of the curve. However, progress baselines that connect the two segments using a sigmoidal function are likely more accurate. In complex, multidomain systems, a single progress baseline is not feasible and spline, cubic or linear functions can be used to connect the different segments (Fig. 11.6AeC). At higher protein concentrations (e.g., 1 mg/mL for an antibody), the use of different functions to connect the folded and unfolded baselines may not have as strong an impact on the resulting shape of the peaks; however, different baseline connecting functions can lead to noticeable differences in both area under the curve and peak shape for lower concentration samples whose peak areas do not extend far above the baseline (Fig. 11.6D and E) The MicroCal (Malvern Panalytical) Origin and PEAQ-DSC packages additionally include the ability to measure DCP by fitting pre- and post-transition baselines. However, DCP determination is generally not as relevant to biopharmaceutical development as rudimentary stability measurements. As noted earlier, DHcal and DCP are thermodynamic terms that apply to proteins that fold and unfold reversibly. The majority of protein systems studied in the pharmaceutical setting do not fit this category. Under nonreversible conditions, DHcal and DCP can still be utilized for a lot-to-lot comparability, but only as area-under-the-curve or baseline change measurements, respectively, and not as thermodynamic parameters.

11.4.2 Solid-DSC data analysis Solid protein thermograms are complex and include glass transitions, crystallization, and melting phenomena. Unlike solution DSC analysis, there are no uniform analysis tools that help deconvolute thermodynamic datadparticularly due to the broad and multifactorial processes that occur during solid heating. Instead, the researcher must become familiar with the various reversing, nonreversing, endothermic, and exothermic events that occur within thermograms of solids to fully interpret the data.

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11.4 Data analysis

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FIG. 11.6 Examples of baseline correction functions and their impact on processed DSC curves. The top figures provide visual examples of different baseline correction functions, spline (A.), progress (B.), and linear (C.), applied to the unfolding curve of a hIgG1 antibody. The lower graphs each display the superpositions of the same samples using each of the three baseline corrections (spline, progress, and linear). The curves on the lower left (D.) are the thermogram temperature dependent heat capacity of the hIgG1 antibody at 0.25 mg/mL, while the curves on the right (E.) are from the thermogram of the same protein at 1 mg/mL.

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11.5 Applications of solution DSC in biopharmaceutical Discovery and Development DSC has a broad range of applications within the Discovery and Development of protein therapeutics. At its basic level, DSC can define whether a protein’s conformational stability will enable manufacturability or bioactivity in vivo. Measurement of Tm and DHU(T) values for therapeutic proteins is a common quality control activity to ensure a lot-to-lot consistency. However, solution DSC is used for much more in the biopharmaceutical industry protein screening, protein engineering, process development, and formulation. This section provides some examples of how DSC is used in these settings.

11.5.1 Antibody therapeutics Antibodies, antibody-fusions, or antibody-like proteins represent the premier protein therapeutics both on the market and in human clinical trials. The utility of antibodies resides in their exquisite target specificity, which enables them to intervene in specific biological pathways with little or no cross-reactivity to other systems. However, it is often underappreciated that antibodies, particularly IgG immunoglobulins, are such marvelous therapeutic entities due to their remarkable biophysical properties. Their high solubility and stability and the necessity that they remain in their folded conformations to be stable and functional, favors their formulation be in a liquid state. As antibodies represent some of the most abundant proteins in serum, they have been available for study since the earliest days of DSC. Antibodies are complex multidomain proteins that consist of two fundamental regions, (1) the antigen-binding fragments (Fabs) that contain the variable regions that interact with antigen and (2) the homodimerization region, also known as the crystallizable fragment or Fc, that interacts with Fc receptors of the immune system. The earliest DSC studies in the 1970s with rabbit polyclonal IgG demonstrated an independence of the unfolding transitions of an antibody’s two Fabs and its Fc [40]. However, only much later, with the advent of recombinant technologies and antibody engineering, did the ability to generate fully human antibodies for DSC analyses become available. Human IgG antibodies consist of four subclasses: IgG1, IgG2, IgG3, and IgG4. Each of these has different stability properties, particularly within their Fc regions [41]; however, the Fc stability properties do not change from antibody to antibody if the subclass is held constant because the sequence is constant (if subtle changes due to allotypes are ignored). It is the Fab regions that typically dictate the stability properties of an antibody due to the large diversity of variable domain sequences that exist to enable different antibodies to specifically bind different antigens. In fact, antibodies generally display a broad range of stabilities based on the variable domains of their Fab regions (Fig. 11.7) [41]. In general, full-length antibodies do not unfold reversibly. Fab domains, in particular, aggregate and often precipitate rapidly when thermally denatured in aqueous solutions. Therefore, Tm and area-under-the-curve are generally reported as relative measures of antibody domain stability since derivation of thermodynamic parameters applies only to reversible protein systems. DSC can be used to screen new antibodies for thermal stability and as a tool for evaluating protein designs for improved stability. For example, we found that a poorly expressed

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FIG. 11.7 Thermal unfolding curves of four human(ized) IgG1 antibodies, BIIB16, BIIB6, BIIB4, and BIIB1. Note the unfolding transitions of the CH2 and CH3 domains for all four IgG1 constructs are identical while the Fab-unfolding transitions are highly variable. Reprinted with permission from Garber E, Demarest SJ. A broad range of Fab stabilities within a host of therapeutic IgGs. Biochem Biophys Res Commun 2007;355:751e7.

antibody that displayed modest stability by DSC could be re-engineering to improve its stability (confirmed by DSC). As a result the protein’s expression was shown to be dramatically improved [42]. Due to their modular nature, antibody fragments like single chain Fvs (the variable heavy chain and variable light chain domains linked recombinantly by a flexible polypeptide) are gaining momentum for their utility within bispecific antibodies. However, these proteins lack stabilizing constant domains and generally have Tm values w25
C lower than a full-length Fab region [43]. Clearly, DSC has become an instrumental tool for the evaluation of antibody engineering designs directed at stabilizing antibody domains [44,45]. DSC has also been successfully employed in the process development and formulation areas of antibody therapeutics. In these areas DSC is a particularly powerful methodd especially with automated assistance for sample handlingdfor evaluating solution conditions for antibody handling. For instance, DSC has shown that the folded states of antibody Fc regions are highly pH-sensitive [46], with clear differences between IgG1 and IgG4. Thus, purification and pH-hold schemes may not be identical between the two subclasses of antibody. To avoid chemical degradation (deamidation, isomerization, etc.), it is advantageous to formulate proteins at pH values below 6.5 for a long-term storage. However, the folded domains of some antibodies can be sensitive to lower pH values; therefore, DSC can be useful in helping to define formulation conditions that compromise between folded stability and chemical stability.

11.5.2 Non-antibody protein therapeutics Similar to antibodies, other therapeutic proteins need to be conformationally stable to be bioactive and manufacturable. Most therapeutic proteins do not have the generally ideal

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stability properties of antibodies and DSC studies that inform protein engineering, process development, and formulation efforts are extremely important. One example is the development of stabilizing formulations for acidic fibroblast growth factor liquid formulation that enabled significantly improved long-term bioactivity [47]. Erythropoietin (Epo) is a reasonably stable therapeutic protein, but like immunoglobulins and many other proteins is sensitive to low pH [48]. Additionally, Epo has a relatively broad thermal unfolding profile that results in a small fraction of unfolded material at pH values that deviate even 1 pH unit from physiological values. Thus, DSC helped define the relatively narrow formulation window for maintaining its bioactivity [48]. Similar studies have been performed with interferon-b to define solution conditions where its folded conformation is maximally stable [49].

11.5.3 Biosimilars A biosimilar product (also called a “follow-on biologic” or “subsequent entry biologic”) is a biopharmaceutical that has been approved by a regulatory agency based on its high similarity to a previously-approved biological product, known as a reference or innovator product. Biosimilar development is not the same process as developing a generic small molecule drug. There is a certain degree of protein variation, even between different batches of the same product, due to the inherent variability of the biological expression system and the manufacturing process. These variations can include differences in post-translational modification and higher order structure. Because of proteins’ high molecular weights, complex structures and natural variations, biosimilars are not as simple to produce as generics. A biosimilar is not an exact duplicate of the reference product, and is thus normally characterized as ‘similar’ or ‘highly similar’ to the reference product. Regulatory agencies evaluate biosimilars based on their level of similarity to their reference products. Due to the complexity of biopharmaceuticals, it is not likely for two different manufacturers to produce two identical products, even if identical host expression systems, processes, and equivalent technologies are used. Biosimilar developers and producers frequently use DSC as a higher order structure biophysical assay to show that a biosimilar has a highly similar DSC thermogram profile (‘fingerprint’), and similar Tm, Tonset, and other thermodynamic parameters, compared to the reference product. DSC was recently used as an analytical tool to compare two monoclonal antibodies and their biosimilars, DSC analysis showed good structural similarity for the reference product and biosimilars [50]. Most biosimilars include DSC data as part of their higher order structure characterization, which has been the case when biosimilars were compared to their following appropriate reference product: etanercept (Embrel) [51], infliximab (Remicade) [52,53], and adalimumab (Humira) [54,55].

11.6 Applications of solid-sample DSC in biopharmaceutical discovery and development Solid-sample DSC is commonly used to characterize freeze-dried biopharmaceutical formulations. As mentioned above, DSC can be used to analyze and optimize the T0 g of a frozen protein solution to ensure that a freeze-dry process occurs at temperatures

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significantly below the T0 g to avoid “collapse” of the product during the formation of a lyophilized cake. Collapse can be associated with loss of product, denaturation, and other detrimental phenomena. Therefore, DSC can be utilized to optimize the lyophilization process to help define the highest temperature possible during freeze-drying to reduce the drying time while keeping the system completely frozen. For frozen solutions (prior to drying), it is imperative to measure the T0 g of the frozen stabilizer solution (generally a sugar) prior to mixing with the protein since stabilizers can depress the T0 g. DSC is also commonly used to evaluate the effect of buffer, bulking agents, stabilizers, and other additives on the stability of the dried formulations. Tg of dried samples typically occurs at temperatures below other phenomena such as crystallization or melting. After drying, the absence of water generally raises the Tg of a biopharmaceutical to a much higher temperature. Examples of solid-DSC profiles of an IgG1 protein are provided in Fig. 11.8 [56]. However, it can be just as important to measure the Tg of the dried solid to ensure it is significantly above the maximum temperature at which the solid is stored. A general rule of thumb is to ensure that the Tg is at least 20
C (some say 40
C) above the maximum storage temperature of a dried solid drug product. A classic example is the analysis performed by Cleland et al. demonstrate the impact of excipients and salts on the lyophilized stability of antibody and protein biologics [31]. Observations of the Tg of freeze-dried solids can be broad and difficult to detect; however, modern DSC designs with higher temperature ramp rates, improved baselines, and modulated temperature control have significantly improved the capability of measuring this difficult parameter. A new application for standard DSCs comes from the growing need for subcutaneous delivery of protein therapeutics, particularly for chronic administration. Subcutaneous

FIG. 11.8 DSC curves of unannealed fresh IgG1/sucrose sample (curve 1) and samples annealed for 20 h at different temperatures. The specific formulation was annealed at 60
C (curve 2), 65
C (curve 3), 75
C (curve 4), 85
C (curve 5), and 90
C (curve 6). The endothermic thermal events around 150
C are likely denaturation endotherms while the thermal events at lower temperatures represent Tg and relaxation events. Reprinted with permission from Wang B, Cicerone MT, Aso Y, Pikal MJ. The impact of thermal treatment on the stability of freeze-dried amorphous pharmaceuticals: II. Aggregation in an IgG1 fusion protein. J Pharm Sci 2010;99:683e700.

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delivery requires extremely high protein concentrations (w100e200 mg/mL) that can impact their biophysical properties due to viscosity and excluded volume effects. Researchers in this area have begun to use standard DSCs with plans to study high-concentration protein solutions since these solutions tend to gel in the DSC cells used for solution measurements causing them to become plugged [57]. The high protein concentrations in subcutaneous formulations can enable the observation of thermal unfolding transitions in standard model DSCs where low protein concentration analyses are impossible.

11.7 Conclusions DSC now plays a major role in the discovery, engineering, manufacturing, and formulation of protein and peptide therapeutics. Recent advancements in DSC design, both in solution-state and solid-state instruments, that improve their sensitivity, their ability to distinguish various phase-transition mechanisms (e.g., reversible vs. nonreversible), and their ease of use (i.e., automated hands-off operation) have dramatically increased their use and impact on the development of biopharmaceuticals. Most protein therapeutics currently entering the clinic have undergone significant recombinant protein engineering for improving their activities, half-lives, and immunogenic profiles. Tools like DSC ensure that these designed molecules remain viable from a physical stability perspective. Additionally, both solution and solid-state DSC are used to help guide the process chemistry and manufacturing of biotherapeutics to conform to limits of a therapeutic’s folded stability or its formulation constraints. DSC has come a long way from the study of basic protein folding thermodynamics to its now broad applicability to the study of protein and peptide therapeutics to maintain the therapeutic properties of these live-saving drugs.

Acknowledgements The authors would like to thank Bill Swartz, Louis Waguespack, and Dile Holton from TA Instruments for helpful discussions and insight.

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