WWW.PHOTOS.COM

Monoclonal antibodies (MAbs) are at the focal point of biologics development. Many of the best-selling drugs are therapeutic MAbs or related proteins (1–2). The combined world-wide sales from MAbs will be nearly US$125 billion by 2020 (3). About 50 MAb products treating a range of diseases have been approved in the United States or Europe. With the large number of MAbs progressing through discovery, biomanufacturers need to accelerate process development and move projects rapidly into clinical manufacturing (4–5). Formulation development, an important aspect of product development, is often on the critical path to successful clinical manufacturing and stability studies, which are essential to investigational new drug (IND) filings. Discussions on leveraging platform processes to shorten process development timelines and save resources often focus primarily on upstream and downstream operations (6–7). Here we describe a rapid platform strategy for formulation development successfully used for MAbs by Patheon Biologics.

Table 1: Summary of excipients used in a number of commercial MAb formulations (6)

Overview of Commercial MAb Formulations
By studying commercial MAb products, we established a rich database for successful MAb formulations. Although every antibody is unique, the molecules are highly similar structurally. Lessons learned from successful examples are invaluable in developing stable and effective formulations for new MAbs.

We summarized 37 formulations that have been successfully used in commercial MAbs, including fragment antigen-binding (Fab) and antibody– drug conjugates (ADCs). Among them, 12 are lyophilized formulations and 25 are liquid formulations, with their concentration ranging from 2 mg/mL to 200 mg/mL. Table 1 lists excipients used in these MAb formulations. Some commonalities can be observed:

• We found six categories of excipients: buffers, salts, surfactants, polyol/disaccharide/polysaccharides, amino acids, and antioxidants.
• Six commonly used buffers keep pH levels between 4.7 and 7.4: acetate, citrate, histidine, succinate, phosphate, and hydroxymethylaminomethane (Tris). Histidine and phosphate dominate (used in 35% and 33% formulations, respectively).
• Most (80%) formulations used one of three surfactants: polysorbate 80 (Tween 80), polysorbate 20 (Tween 20), and poloxamer 188. Among those, 72% used polysorbate 80.
• All lyophilized (freeze-dried) formulations used one or a mixture of polyol/disaccharide/polysaccharide (e.g., mannitol, sorbitol, sucrose, trehalose, and dextran 40). Sucrose was the most popular excipient and was included in over 80% of these formulations. Sugars provide bulk for lyophilized formulations and serve as stabilizing agents for therapeutic proteins. This category of excipients also is used in liquid formulations (found in 30%).
• Sodium chloride (NaCl) is commonly used. We found it in about 50% of formulations.
• Two amino acids (glycine and arginine) were used in about 20% of the MAb formulations.
• Antioxidants used included ascorbic acid, methionine, and ethylenediaminetetraacetic acid (EDTA), a chelating agent, presumably intended to prevent heavy metal-induced oxidation). These are used infrequently, however, with each of the three antioxidants found in only one formulation.

A Platform Approach
At Patheon Biologics, we developed a formulation development platform for rapid formulation screenings for MAbs. Our approach consists of screening studies for pH and excipients followed by in-depth evaluation of buffers and excipients.

Stage One Screening Study to Identify the Optimal pH: We begin by formulating a MAb in a citrate–phosphate mixture at different pH levels. Within the normal pH range, citrate has an acid-dissociation constant (pK_a) value at 4.8 and 6.4, and phosphate has a pK_a at 6.8. The mixture of the two buffering salts allows us to control pH well in the range of 4–8. Because all these formulations have the same buffer composition, the effect of buffer types on MAb stability is eliminated. We stress our formulations at 50 °C for a week and analyze by potential stability-indicating methods. We study appearance, measure subvisible particles (e.g., using a HIAC particle counter from Beckman Coulter), and apply several assay methods: differential scanning calorimetry (DSC) (e.g., using a MicroCal VP-DSC capillary DSC system from Malvern Instruments), sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), binding enzyme-linked immunosorbent assay (ELISA), and size-exclusion and cation-exchange high-performance liquid chromatography (SEC-HPLC, CEX-HPLC). Thus we select optimal pH conditions for each MAb. We also evaluate each analytical method for its ability to detect MAb degradation. Only sensitive stability-indicating methods will be used in subsequent studies.

Stage Two Screening Study to Pick Out Stabilizing Excipents: Using the pH selected above, we select up to two buffers for this study. For example, if the target pH is 6, then we use both histidine and citrate based on their pK_a values.

We also choose one or two excipients from each category identified above. For example, we might add sucrose (from the polyol/ disaccharide family), polysorbate 80 (from the surfactant family), glycine and arginine (from the amino acid family), sodium chloride (NaCl, a salt), and methionine (from the antioxidant family) to the buffer(s) for study in this step. We typically use a screening design of experiment (DoE) study. At the end of the screening study, one or two stabilizing excipients and buffers are selected for the next stage of formulation development.

Stage Three In-Depth Evaluation of the Most Stabilizing Buffer(s) and Excipient(s): We use a response-surface methodology (RSM) DoE study to explore the optimal excipient concentration and interactions among excipients. And we run another one-week thermal stress study at 50°C. The MAb is formulated at a target concentration and filled into the intended vial/stopper system to mimic a final-product configuration. By the end of this study, we will have chosen a final lead formulation. MAbs are typically stable over freeze–thaw cycles and agitation in most formulations. Nevertheless, we will also confirm the lead formulation’s stability over freeze–thaw and agitation.

Turnaround time for this rapid yet rational and relatively comprehensive approach is about 12 weeks. We have successfully used the platform approach to formulate several MAb products.

Figure 1: A typical differential scanning calorimetry (DSC) thermogram for a monoclonal antibody (MAb) showing the enthalpy change; the major peak is caused by unfolding of both the Fab part and CH2 domain, and the minor peak is unfolding of the CH3 domain.

A Case Study
We applied our approach and successfully developed a 100-mg/mL liquid formulation for a client’s MAb. Through the above-described studies, we identified its optimum pH to be pH 6. We also narrowed down the buffer and excipients.

Next, we performed a DoE study based on previous results with the MAb formulated at pH 6.0 and 100 mg/mL. Using an integrated optimal (I-optimal) experimental design, we studied excipients comprising 10–50 mM histidine, 1–2% trehalose, and 10–50 mM phosphate–citrate buffer. Based on known protein unfolding onset temperatures obtained from DSC in the screening study (Figure 1), we used 45 °C as the thermal stress temperature for this product, extending the incubation time to 14 days.

Figure 2: Contour plot of the effects of histidine and citrate–phosphate buffers on high–molecular-weight (HMW) species

Figure 3: Contour plot of the effects of histidine and citrate–phosphate buffers on monomer content

Figure 4: Contour plot of the effects of histidine and citrate–phosphate buffers on low–molecular-weight (LMW) species

Figure 5: Prediction variance profile of size-exclusion chromatographic (SEC) data by JMP software analysis

Statistical analysis of second-stage data (Figures 2–5) demonstrated that neither citrate–phosphate nor trehalose concentrations significantly influenced this MAb’s stability. Histidine had a substantial positive effect in inhibiting the formation of
aggregates (p = 0.0006) but an insignificant effect on protein fragmentation (p = 0.1231). Within the tested range (10–50 mM), higher concentrations of histidine provided better protein stability. So we selected a histidine-containing formulation and performed a 24-month real-time stability study. Figure 6 shows the resulting data for this 100-mg/mL MAb formulation.

Figure 6: 24-month stability study results of a 100-mg/mL MAb formulation

Stability study results from chosen assays demonstrated that the antibody had long-term stability in this formulation. We developed the formulation within just 12 weeks through this platform formulation development approach.

A Strategic Approach
Based on an analysis of 37 commercial MAb formulations along with our own extensive experience, our team at Patheon Biologics established a rapid platform formulation-development strategy for MAbs. This three-step process begins with pH screening followed by excipient screening, and concludes with a more in-depth evaluation of the best stabilizing buffer(s) and excipient(s). In just a few weeks, researchers can develop a stable formulation for MAb product development.

Acknowledgments
We thank Dr. Michiel Ultee (Ulteemit Bioconsulting, LLC), Dr. David Kenyon (Patheon Biologics), and Paul Jorjorian (Patheon Biologics) for their valuable review and comments.

References
1 Aggarwal S. What’s Fueling the Biotech Engine — 2012 to 2013. Nat. Biotechnol. 32(1) 2014: 32–39. doi:10.1038/nbt.2794.

2 Das RC, Morrow, Jr. KJ. Therapeutic Antibodies in Review: Innovative Products and a Range of Indications Drive the Therapeutic Antibody Market. BioPharm Int. 26(2) 2013.

3 Ecker DM, Jones SD, Levine HL. The Therapeutic Monoclonal Antibody Market. MAbs 7(1) 2015: 9–14. doi:10.4161/19420862.2015.989042.

4 Glennie MJ, Johnson PW. Clinical Trials of Antibody Therapy. Immunol. Today 21(8) 2000: 403–410.

5 Reichert JM. Antibodies to Watch in 2014. MAbs 6(4) 2014: 799–802. doi:10.4161/MAbs.29282.

6 Steinmeyer DE, McCormick EL. The Art of Antibody Process Development. Drug Discov. Today 13(13–14) 2008: 613–618. doi:10.1016/j.drudis.2008.04.005.

7 Rathore AS, et al. Evolution of the Monoclonal Antibody Purification Platform: The Authors Discuss the Evolution of the Purification Platform for Manufacturing of MAb Therapeutics. BioPharm Int. 26(11) 2013.

Jichao Kang, PhD, is director of analytical and formulation development, where Xi Lin is a senior scientist and Jason Penera is a scientist II, at Patheon (a business unit of DPx), 4815 Emperor Boulevard, Suite 300, Durham, NC 27703-8580; 1-919-226-3200; jay.kang@patheon.com; www.patheon.com.

The post Rapid Formulation Development for Monoclonal Antibodies appeared first on BioProcess International.

Photo 1: Standard one-bag position in chamber

It is well understood that freeze– thaw processes affect the product quality of biopharmaceuticals (1–3). It has been reported that there is no consistent method of controlled freezing and thawing rates for biological formulations (4). Traditionally, ultralow temperature storage chambers that were not designed for freezing have been used to provide an energy state for the environment surrounding the product with very little excess capacity to change the state of the product.

This study details a consistent method for controlled-rate freezing and thawing. It also includes the effect of load and container position on freeze rates. The freeze–thaw controlled-rate chamber used in this study was a model 4002 manufactured by Farrar Scientific. It permits rapid, uniform bulk freezing and/or thawing of products with temperature ranges from +40 °C to –80 °C. It has a minimum of 1.7 kW of net cooling and heating capacity over the entire temperature range and offers the flexibility of programmable profiles.

Photo 2: Thermocouple placement in bag

Experimental  Procedure
We explored the effect of the program temperature profile on the rate of product freezing and thawing. Testing various temperature profiles helped determine the best profile for uniformly controlling the freezing rate to the first phase transition (0 °C) and the cooling rate from the first phase transition to –35 °C. The target product cooling rates from –5 °C to –35 °C were 0.13–0.94 °C/min. The target thaw rate was to be as fast as possible without overshooting a particular temperature.

Using tap water simulated a waterbased biological formulation. Bags with 16-L fill capacity were used in this study, with each bag placed on a stainless steel shelf. For Test 1, the bag was filled with 16 L of water and arranged to maximize air flow to the entire outer surface as uniformly as possible (Photo 1). A thermocouple was placed in the middle port with the tip located halfway between the bag midpoint (hole) and the edge (Photo 2). The end of the thermocouple is also equal distance from the upper to the lower bag surface (z direction). This is the expected last point to freeze.

Figure 1: Controlled-rate freeze, Test 1 (one 16-L bag)

Controlled Freeze Rate Results and Discussion
Figure 1 shows results for controlled-freeze Test 1. Notice that it took 472 minutes to break from the phase change and 732 minutes to reach –35 °C. Table 1 shows cooling rates from –5 °C to –35 °C. The cooling rates are controlled at 0.08 °C/min to 0.30 °C/min. The cooling rate is at its slowest rate from –30 °C to –35 °C because the difference in air temperature (–40 °C) and product temperature is so small. Therefore, Test 2 was conducted where the second plateau

Photo 3: Standard eight-bag positions in chamber

temperature was changed from –40 °C to –50 °C (Figure 2). Table 2 shows those cooling rates, which fall within the target range of 0.13 °C/min to –0.94 °C/min throughout the entire temperature range from –5 °C to –35 °C.

Table 1: Test 1 cooling rates

Figure 2: Controlled-rate freeze, Test 2 (one 16-L bag)

Because the correct temperature profile was determined with one bag, the next test (Test 3) used the same temperature profile with eight bags. For the eight–bag test, the bag’s positions were set to maximize air flow to the surface of all bags as uniformly as possible (Photo 3). The bags were numbered one to eight in consecutive order from top (Bag 1) to bottom (Bag 8).

Figure 3: Controlled-rate freeze, Test 3 (eight 16-L bags)

Figure 3 and Table 3 show the cooling rates of each bag. The eight bags break from the phase change in the range of 429 to 555 minutes. The one 16-L bag (Test 2) broke from the phase change at 499 minutes — within the range of the eight bags. This shows that the freezing rate is independent of product load. The cooling rate from 5 °C to 35 °C ranged from 0.13 °C/min to 0.73 °C/min. All bags had cooling rates within the target range. These results show that the cooling rate is not dependent on bag position in the chamber or load.

The top bag (Bag 1) was the fastest to break from the phase transition and the fastest to cool from –5 °C to –35 °C.

Table 2: Test 2 cooling rates

This is to be expected because the air flow enters the chamber from the top left corner and flows down and out of the left duct toward the right side. What we did not expect was that the bottom bag was not the slowest to break from the phase transition. Bag 7 was the slowest to break from the phase transition. Even though the bottom bag has very little air flow underneath it, it apparently has a higher velocity air flow in contact with the top side of the bag compared that of Bag 7. Bag 2 had the slowest cooling rate from –5 °C to –35 °C even though it was the second to break from the phase transition. It appeared that the other bags that were still warmer slowed Bag 2’s cooling rate until they also reached a lower temperature.

Table 3: Test 3 bag cooling rates

Table 4: Test 4 bag cooling rates

Figure 4: Controlled-rate freeze, Test 4 (eight 16-L bags)

To determine whether the cooling rates could be increased without exceeding the maximum (0.9 °C/min) target, Test 4 decreased the temperature as quickly as possible (to –80 °C in 10 seconds). Figure 4 and Table 4 show those cooling-rate results. All bags had cooling rates from –5 °C to –35 °C that were in the target range (0.13–0.94 °C/min). These results show again that the cooling rates were controlled to the desired target range independent of bag position in the chamber. In addition, the temperature at which each bag breaks from the phase transition ranges from 209 minutes (Bag 2) to 291 minutes (Bag 7). These results also show that freezing rates were controlled to a reasonable level.

Figure 5: Controlled-rate freeze, Test 5 (eight 16-L bags)

Another test was conducted (Test 5) to extend the time before bags break from the phase transition. The temperature profile had a plateau at –15 °C for eight hours before plunging to –80 °C. Figure 5 and Table 5 show Test 5 controlled-cooling results. The temperature at which each bag breaks from the phase-transition ranges from 562 minutes (Bag 3) to 652 minutes (Bag 7). It took 353 more minutes for the first bag to break from the phase transition and 361 more minutes for the last bag to break from the phase transition compared to the results of Test 4. The cooling rate for all bags from –5 °C to –35 °C were again within the target range.

Table 5: Test 5 bag cooling rates

Photo 4: Standard four-bag positions in chamber

Because the correct temperature profile was determined with eight bags, Test 6 used the same temperature profile with four bags. For this test, bags were arranged to maximize air flow to the surface of all bags as uniformly as possible (Photo 4). The bags were numbered one–four in consecutive order from top (Bag 1) to bottom (Bag 4). The temperature profile was programmed to match the actual temperature profile of Test 5 (see box above).

Table 6: Test 6 bag cooling rates

Figure 6: Controlled-rate freeze, Test 6 (four 16-L bags)

Figure 6 and Table 6 show the cooling rates of each bag in Test 6. The temperature at which each bag breaks from the phase transition ranges from 575 minutes (Bag 2) to 618 minutes (Bag 1). This is within the range reported in Test 5. This again shows that the freezing rate is independent of product load. In addition, all bags had cooling rates from –5 °C to –35 °C within the target range. These results show that the cooling rate was not dependent on bag position in the chamber or load.

Figure 7: Controlled-rate thaw, Test 7 (eight 16-L bags)

Controlled Thaw Rate Results and Discussion
Fast thaw rates are advised for preventing damage to biological formulations (1). However, most biological formulations are temperature sensitive. This study shows how the maximum temperature of the product as well as controlled-thaw rates could be controlled. The thaw test temperature profile starts with product temperatures at –80 °C and ramps up to +20 °C as quickly as possible. For the purpose of this study, the time it took for each bag to reach +2 °C is recorded as the thaw time. Figure 7 details Test 7 thaw results in which the eight bags reached +2 °C in the range of 599 minutes (Bag 2) to 890 minutes (Bag 7). As was the case with the slower freezing of Bag 7, its position in the chamber provides for the least amount of air flow. Seven out of the eight bags show tight thaw-rate control with only 124 minutes separating the first bag from the seventh bag to thaw.

Figure 8: Controlled-rate thaw Test 8 (four 16-L bags)

To test the effect of load, thaw Test 8 was conducted with the same conditions using four bags (Figure 8). They reached +2 °C in the range of 557 minutes (Bag 1) to 749 minutes (Bag 4). Only Bags 1 and 2 thawed a little faster than any of the eight bags in Test 7. However, this is still a relatively good thaw-rate control regardless of load. In addition, the thaw rate was controlled independent of the bag position in the chamber.

Figure 9: Controlled-rate thaw, Test 9 (one 16-L bag

To further test the effect of load, thaw Test 9 used the same conditions with one bag (Figure 9). The bag reached +2 °C in 597 minutes, which is within the range of all 4 bags in Test 8. This shows that thaw rate was controlled independent of load.

Freezing and Thawing Biological Formulations
A method was successfully developed to control the desired freezing and thawing rate using a controlled–rate freeze–thaw chamber. It was validated with different loads (one, four, and eight bags) and product positions in the chamber. A program was developed that a client can use to reliably and uniformly bulk freeze and thaw its biological formulation.

References
1 Cao E, et al. Effect of Freezing and Thawing Rates on Denaturation of Proteins in Aqueous Solutions. Biotechnol. Bioeng. 82(6) 2003: 684–690.

2 Radmanovic N, et al. Understanding the Freezing of Biopharmaceuticals: First-Principle Modeling of the Process and Evaluation of Its Effect on Product Quality. J. Pharma. Sci. 102(8) 2013: 2495–2507.

3 Reinsch H, et al. Examining the Freezing Process of an Intermediate Bulk Containing an Industrially Relevant Protein. Enzyme Microb. Tech. 71(4) 2015: 13-19.

4 Puri M, et al. Evaluating Freeze–Thaw Processes in Biopharmaceutical Development: Small–Scale Study Designs. BioProcess Int. 2015: 1–16.

Jerry King is a senior scientist at Farrar Scientific, 30765 State Route 7, Marietta, OH 45750; jking@farrarscientific.com, 1-740-374-8300.

The post Validation of Controlled Freezing and Thawing Rates: A 16-L–Bag Study appeared first on BioProcess International.

In perspective: aspirin (upper left, front), somatropin (middle molecule), and an antibody (lower right, back) — to approximate scale — molecular models from Wikicommons

Over recent decades, protein-based therapeutics have emerged as key drivers of growth in the pharmaceutical industry. Drug development pipelines have filled with biologics, and a handful of monoclonal antibody (MAb) products have become some of the best-selling drugs around the world. Production of biotherapeutics is often challenging because of the inherent instability of these large, complex molecules. Their fragile nature has forced manufacturers to change how bulk drug substances (BDSs) are handled and final drug product is formulated, sterile filtered, and filled. Ajinomoto Althea was established in 1998 as a contract manufacturer that primarily focused on one class of therapeutics: large-molecule biologics. Here we share some learnings from over 17 years of experience in biomanufacturing and outline special considerations necessary for high-quality biologics fill and finish.

The biologics market is growing 10–15% year over year and now accounts for an estimated 20% of all pharmaceutical sales (1, 2). In 2014, 41 biologics were approved for market in the United States alone, the highest number launched in one year since the 1990s (3). “Big pharma” continues to invest in biologics development programs and manufacturing capabilities (4). Development pipelines are robust, with more than 900 biologic products in development, and all indications point to continued growth of this segment (5).

Table 1: Comparing the complexity of different manufacturing processes

Biopharmaceuticals are manufactured by or extracted from living sources. These products are typically proteins, nucleic acids, vaccines, cells, or viruses. In general, when compared with traditional chemically synthesized drugs, biomolecules are larger and more complex, and their quality attributes depend on the biomanufacturing process. One commonly used analogy to help illustrate the complexity of biotherapeutics is to compare their relative sizes with other manufactured goods. Table 1 illustrates that moving from production of small molecules such as aspirin to MAbs such as trastuzumab (Genentech/Roche’s Herceptin), the magnitude of difference is comparable to going from manufacturing bicycles to private jet planes.

Not only are biologics more difficult to produce, with more complex supply chains, but they are also significantly more expensive to develop and manufacture. Because of that demonstrated size as well as the complex physicochemical interactions between a protein and its environment, biologic-based therapeutics must be handled differently from their small molecule counterparts.

Protein Structure and Stability
Protein therapeutics are high– molecular-weight (HMW) polypeptides that require a specific three dimensional structure for their biologically activity. Their structures are considerably more complex than small molecules, consisting of four different structure levels: primary, secondary, tertiary, and quaternary (6, 7).

Primary structure describes the linear sequence of amino acids that make up a protein. Human proteins are made from 20 standard amino acid residues, all differing from one another by specific molecular side chains. A protein’s unique sequence defines its structure and function.

Secondary structure refers to regular local substructures within protein molecules defined by hydrogen bonds between amide hydrogens and carbonyl oxygens. The most common secondary structures are alpha helices and beta sheets.

Tertiary structure describes the overall three-dimensional structure of an entire protein. Its folding is mainly driven by hydrophobic interactions but also stabilized by hydrogen bonds, salt bridges, and disulfide bonds.

Many proteins are made up of multiple polypeptide chains. Quaternary structure refers to how those protein subunits interact with each other and are arranged to form a larger complex. The conformation of protein complexes is stabilized by the same interactions that drive the tertiary structure of their components.

Preventing degradation is essential in maintaining safety and efficacy during the manufacture and long-term storage of a protein therapeutic (7–10). Numerous factors can negatively affect protein stability. A protein’s amino acid backbone (primary structure) can be changed by formation and destruction of covalent bonds (11). Such chemical modifications can be caused by oxidation, deamidation, peptide-bond hydrolysis, disulfidebond exchange, and cross-linking (6, 8–10).

Because the interactions that drive and stabilize higher-order secondary, tertiary, and quaternary structures are inherently weak interactions, proteins are susceptible to physical and conformational degradation. A number of external factors can cause physical degradation: excessively high temperatures, varying acidity or alkalinity, mechanical agitation, high shear forces, and the presence of hydrophobic molecular surfaces, leachables/extractables, nonpolar solvents, and certain excipients (6, 8–10). Proteins are also “sticky” and prone to both adsorption (adherence to surfaces, 10) and aggregation (clumping together, 7–10, 12) because of their hydrophobicity in solution. Aggregates are of special concern because they have been demonstrated to associate with altered biological activity and increased immunogenicity (6, 7, 8, 10).

The potential for protein aggregation can be increased by a number of mechanisms (6, 7, 8, 10, 12–14):

• exposure to liquid–air, liquid– solid, and liquid–liquid interfaces
• mechanical stresses such as stirring and pumping
• freeze–thaw cycles
• solution conditions that affect the rate and number of aggregates formed
• interactions with metal surfaces
• the presence of certain ligands.

At high concentrations, protein–protein interactions will significantly increase the viscosity of a BDS, which in turn decreases its manufacturability and complicates drug delivery (12). Exposure to light can trigger chemical and physical degradation because certain amino acid residues (tryptophan, tyrosine, phenylalanine, and cysteine) are susceptible to photooxidation (8, 9). And in some instances, these factors can work synergistically, one event triggering another mechanism to follow (8). For example, a hydrophobic surface can cause a protein to become misfolded, which can lead to aggregation.

Fill–finish operations must be designed with an awareness of the innate properties of proteins and external factors that can affect a given protein’s behavior and stability (8, 10). Special processes, procedures, and equipment must be in place to ensure product integrity during fill–finish manufacturing.

Figure 1: Schematic of drug product (fill–finish) manufacturing

Special Considerations for Fill–Finish of Biologics
Preservation of a protein’s three-dimensional (3D) structure is of vital importance during fill and finish. Because of the many conditions that can negatively affect therapeutic protein structure, function, and stability, very special care must be taken at every step of the biomanufacturing process (6, 7, 8 , 10). Figure 1 illustrates numerous steps and manipulations in biologic drug-product manufacturing.

For biologics manufacturing, special handling procedures must be in place for receipt of an incoming BDS. Quality control (QC) operators are trained in proper BDS handling and visual inspection according to a good manufacturing practice (GMP) specification sheet. Specifications typically cover container integrity, storage conditions (e.g., liquid or frozen), the number of containers, and so on. Once accepted by a QC group, BDS is transferred to an appropriate storage location until its scheduled filling date. Procedures for managing nonconforming biologic BDS also should be in place as part of a manufacturer’s quality system oversight.

End-to-end cold-chain infrastructure and traceability are critical to maintaining the quality of a biologic BDS and drug products (14, 15). Validated storage at 2–8 °C, –20 °C, and –80 °C is sufficient to support most protein biologics. Procedures should include 24-hour temperature monitoring, notification procedures to follow in case of failure, and redundant systems for refrigeration and emergency power. A drug-product manufacturer needs routine preventive maintenance plans to ensure that its equipment is working properly. Intermediate storage for extended hold-times or multiple-day filling processes will require appropriate transfer and storage procedures to maintain sterility and optimal temperature of the BDS.

Freezing a BDS brings a number of advantages in biologics manufacturing: decoupling a BDS from the drugproduct manufacturing process, reducing the risk of microbial contamination, and increasing BDS stability for flexible timelines (8). However, detailed thawing procedures should be in place to ensure that thawing frozen a BDS doesn’t compromise protein activity and stability based on data acquired during product development (13). Once a BDS is thawed, then associated dilution, pooling, and formulation protocols must be gentle to prevent foaming or splashing of the protein solution. Introduction of high shear forces can induce a protein to aggregate or cause conformational changes that affect its activity and/or solubility (11). Thus, low-shear mixing methods are required. Ideally, freezing and thawing steps are both performed in closed systems to prevent exposure of a BDS to outside air, thereby reducing the potential for microbial contamination (16).

Accurate sizing of filters for sterile filtration is important to reduce hold-up and loss of high-value biologic BDS. In both sterile filtration and container–closure filling unit operations, peristaltic pumps have become the standard for most biologics filling processes. Adjusting flow speed to prevent foaming and splashing is simpler with peristaltic than with piston pumps (17). Cleaning and changeover becomes more efficient with such pumps as well because their disposable tubing, connectors, and filling needles are the only materials that directly contact the product (17). And as mentioned above, most biologics are sensitive to shear forces. Peristaltic pumps provide low-pressure pumping for gentler drug product manufacturing (17).

For biologics-based parenteral fill–finish products, manual visual inspection is standard in both the United States and Europe. This QC method relies heavily on the experience, training, and skill of operators (18). Regulators expect a well characterized and robust inspection process. Extensive training is required along with a robust qualification regimen to ensure that operators have the skills necessary to detect a long and thorough list of potential drug-product and container defects (18).

Some challenges facing biologics fill and finish manufacturing operators include

• handling of sensitive biologic products
• inspection of clear and opaque suspensions
• inspection of amber vials
• distinguishing between foreign and product-related particulates.

Labeling, packaging, and shipment processes all must adhere to GMP regulations. Proper handling is required to eliminate unnecessary agitation that can adversely affect stability. Throughout preparation and shipping of drug product, the “cold chain” must be maintained. That is, the product must not be subject to temperature fluctuations. Also critical during transport is secure packaging with appropriate protection against temperature fluctuations and mechanical agitation (14, 15).

Temperature monitors, data loggers, and global-positioning system (GPS) trackers also should be included when appropriate. Active, validated shipping containers such as shipping containers from CSafe Global have been proven to transport valuable biologics without compromising product quality.

Other Critical Aspects
Highly concentrated protein solutions can have viscosity levels that present special challenges in drug product fill–finish operations (12, 19). Pumping viscous solutions through small-diameter tubing can generate shearing and other effects that could degrade proteins. Using tubing of different Shore hardness (see “Definitions” box), diameters, and configurations often helps manufacturers handle viscous product solutions. Performing critical unit operations at higher temperatures can reduce viscosity, but a manufacturer must determine the effect of such conditions on a given protein’s stability and activity before choosing that option (19).

Most protein biotherapeutics are made by microbial fermentation or mammalian cell culture. The raw materials, nutrient media, and growth conditions used for such production processes can promote the growth of unwanted microorganisms as well as desirable expression systems (10, 16). Microbial contamination can affect the robustness and repeatability of a biomanufacturing process as well as its final drug product’s purity, potency, and safety (16). Adequate control measures must be in place to prevent microbial growth in biological facilities and equipment (16). They can include a robust cleaning schedule with a set rotation of disinfectant/cleaning agents along with controls that ensure sterility of growth environments before culture inoculation. Low bioburden must be maintained throughout the entire drug-product manufacturing process — from BDS receipt through aseptic filling (16).

Because biologics developers are focusing increasingly on personalized medicine and orphan-drug designations, the industry has seen a significant increase in the number of lower-volume products introduced to market. And many companies outsource their drug-product manufacturing activities even when drug substance is manufactured “in-house.” So most fill–finish operations are performed in multiproduct facilities. Most suchfacilities have converted to single-use technologies at least in part because disposable systems offer many benefits: reducing contamination risk, increasing operational flexibility, and shortening changeover times (20, 21). Efficiencies gained can facilitate and speed up biologics’ movement through clinical trials to commercial launch, therefore increasing a sponsor company’s return on investment.

Maintaining protein stability must take into account many complex physicochemical phenomena that are controlled by both intrinsic and extrinsic factors. Because of the significant number of manipulations necessary for biologics fill and finish, such operations carry the potential risk of disrupting the biologically active state of a therapeutic protein. Formulation, filtration, and filling of drug products is an often overlooked but vital part of biologics development that requires special capabilities to ensure the high quality of products throughout manufacturing, transport, and long-term storage.

Since 1998, Ajinomoto Althea has invested significant resources to build manufacturing platforms that specifically protect the integrity of large-molecule biologics. Clients have come to rely on this experience and expertise to ensure high-quality fill–finish manufacturing of their high-value biotherapeutics.

Commonly Outsourced
The overall biologics market is growing at a 10–15% rate. After analytical testing, drug product fill and finish is the most outsourced activity in biologics, and it is predicted to grow for the foreseeable future. That will translate to steady growth for the outsourced fill–finish market. Contract manufacturing organizations (CMOs) that have the requisite biologics expertise, regulatory track record, and capacity have an opportunity to gain business in this high-growth segment. Since 2003, Althea has been a leading expert in aseptic drug-product filling in both vials and syringes. The company’s experience, expertise, and regulatory track record have brought tremendous revenue growth over the past few years. We anticipate this growth to continue through the coming years.

References
1 Thomas A. Global Biological Drugs Market Will Reach US$287 Billion in 2020. Persistence Market Research: New York, NY, 22 June 2015; www.persistencemarketresearch.com/mediarelease/global-biological-drugs-market.asp.

2 Biologic Therapeutic Drugs: Technologies and Global Markets. BCC Research LLC: Wellesley, MA, January 2015; www.bccresearch.com/market-research/biotechnology/biologic-therapeutic-drugs-technologies-markets-report-bio079c.html.

3 Carroll JD. Biopharma Posts a ChartTopping 41 New Drug Approvals in 2014. Fierce Biotech 2 January 2015; www.fiercebiotech.com/special-reports/biopharma-posts-chart-topping-41-new-drug-approvals-2014/2015-01-02.

4 Van Arnum P. Big Pharma’s Investment in Biologics. PharmTech.com 3 May 2013; www.pharmtech.com/big-pharmas-investment-biologics.

5 Medicines in Development: Biologics. Pharmaceutical Research and Manufacturers Association: Washington, DC, 2013; www.phrma.org/sites/default/files/pdf/biologics2013.pdf.

6 Technical Brief Volume 8. Protein Structure. Particle Sciences Drug Development Services: Bethlehem, PA, 2009; www.particlesciences.com/news/technical-briefs/2009/protein-structure.html.

7 Watts A. Biological Drugs: Practical Considerations for Handling and Storage. University of Bath Department of Pharmacy and Pharmacology: Bath, UK, 22 May 2013; www.slideshare.net/bathasu/biological-drugs-practical-considerations-for-handling-and-storage.

8 Patel J, et al. Stability Considerations for Biopharmaceuticals, Part 1: Overview of Protein and Peptide Degradation Pathways. BioProcess Int. 9(1) 2011: 20–31; www.bioprocessintl.com/manufacturing/formulation/stability-considerations-for-biopharmaceuticals-part-1-332821.

9 Kerwin BA, Remmele RL. Protect from Light: Photodegradation and Protein Biologics. J. Pharmaceut. Sci. 96(6) 2007:1468–1479; doi:10.1002/jps.20815.

10 Kashi R. Challenges in the Development of Stable Protein Formulations for Lung Delivery. AAPS Symposium (Baltimore, MD, 9 September 2011); http://docplayer.net/11725148-Challenges-in-the-development-of-stable-protein-formulations-for-lung-delivery.html.

11 Maa YF, Hsu CC. Effect of High Shear on Proteins. Biotechnol. Bioeng. 51(4) 1996: 458–465; www.ncbi.nlm.nih.gov/pubmed/18629798.

12 Child J. Minireview: Protein Interactions (honors thesis). University of New Hampshire Scholars’ Repository: Durham, NH, Fall 2012; http://scholars.unh.edu/cgi/viewcontent.cgi?article=1081&context=honors.

13 Puri M, et al. Evaluating Freeze–Thaw Processes in Biopharmaceutical Development: Small-Scale Study Designs. BioProcess Int. 13(1) 2015: 34–45; www.bioprocessintl.com/manufacturing/fill-finish/evaluating-freeze-thaw-processes-biopharmaceutical-development-small-scale-study-designs.

14 Butschli J. Assessing Trends in Temperature-Sensitive Biologic Shipments. Life Sciences Logistics 31 August 2015; www.lifescienceslogistics.com/logistics/temp-control-packaging/assessing-trends-temperature-sensitive-biologic-shipments.

15 Burgess B. Packaging Trends for Biologics. Healthcare Packaging 9 April 2013; www.healthcarepackaging.com/applications/healthcare/packaging-trends-biologics.

16 Lolas A. Microbial Control Strategies in Bioprocessing Falling Short of Assuring Product Quality and Satisfying Regulatory Expectations. Am. Pharmaceut. Rev. 2 April 2013; www.americanpharmaceuticalreview.com/Featured-Articles/134040-Microbial-Control-Strategies-in-Bioprocessing-Falling-Short-of-Assuring-Product-Quality-and-Satisfying-Regulatory-Expectations.

17 Lambert P. Dispensing Biopharmaceuticals with Piston and Peristaltic Pumps. PharmTech.com 17 September 2008; www.pharmtech.com/dispensing-biopharmaceuticals-piston-and-peristaltic-pumps.

18 Forcinio H. Trends and Best Practices in Visual Inspection. PharmTech.com 2 March 2014; www.pharmtech.com/trends-and-best-practices-visual-inspection.

19 Palm T, et al. The Importance of the Concentration-Temperature-Viscosity Relationship for the Development of Biologics. BioProcess Int. 13(3) 2015: 32–34.

20 Langer ES, et al. 12th Annual Report and Survey of Biopharmaceutical Manufacturing Capacity and Production. BioPlan Associates, Inc.: Rockville, MD, April 2015; http://bioplanassociates.com/publications/12th_Biomfg_Table_of_Content.pdf.

21 Chao S-B. Biomanufacturing Vision for the Future. June 2013; NIPTE/FDA Research Conference: Future of Pharmaceutical Manufacturing (Rockville, MD, 18–19 June 2013). www.nipte.org/sites/default/files//documents/Biomanufacturing Vision for the Future – Shou-Bai Chao(1).pdf.

Don Paul Kovarcik is a technical marketing specialist at Ajinomoto Althea, Inc., 11040 Roselle Street, San Diego, CA 92121; 1-858-882-0123, fax 1-858-882-0133; donpaul.kovarcik@altheacmo.com.

The post Critical Factors for Fill–Finish Manufacturing of Biologics appeared first on BioProcess International.

with Dr. Josefina Nilsson

For this webcast, Josefina Nilsson (EM Services business unit head) discussed Vironova’s work, including case studies. She focused on characterization of drug and gene delivery platforms with electron microscopy and image analysis, specifically for systems that use viral vectors or liposomes. Along with two colleagues — Gustaf Kylberg (image analysis expert) and Mathieu Colomb-Delsuc (electron microscopist) — she then answered questions from the audience.

Nilsson’s Presentation
Structural characterization provides important insights into the quality of development and manufacturing practices, including encapsulating systems. Important characteristics include morphology, integrity, sample purity, and the ratio between filled and empty particles. Our analysis can measure unique parameters specific to certain delivery systems such as lipid-based and viral vectors. Using electron microscopy and image analysis, you can gain objective, repeatable, and quantitative structural information cost efficiently.

We start by preparing samples and acquiring images using electron microscopy. Then we use our in-house developed software for image analysis. The program automatically detects a large number of particles to reach statistical significance and classifies each type within the obtained image. It can generate a report of the analysis that includes representative images with a quantitative analysis, statistics, and summary.

Case Studies: Our first study looks at samples using adenoviruses with different formulations. One formulation had adenovirus particles with good integrity, nice morphology, and particles acting as a single unit. However, when the formulation was changed, those particles started to break down and aggregate.

Another study involved samples using an adenoassociated virus (AAV) viral vector taken during purification both before and after sample filtration. Although the images were quite “noisy,” our software was able to detect and categorize different particles: AAVs, aggregates, and protein-based impurities. In this study, filtration increased aggregation from stress on the samples rather than removing aggregates.

Additionally, our analysis can measure the size and shape of particles, classify them, and differentiate between filled and empty particles using cryoelectron microscopy. For lipid-based systems, we can perform lamellarity analysis and measure the thickness of lipid bilayers. The resulting information can help us determine the optimal storage conditions for such particles to maintain their dimensions.

Electron microscopy and image analysis can provide all the structural information needed for effective and high-quality research and development and manufacturing of gene and drug delivery systems.

Questions and Answers
Do you prepare GMP reports of that analysis as part of the service? Yes.

How do you ensure that images are representative? We always try to make the image as representative as possible by exploring the whole area of a grid and looking at areas with different ice thickness. What we provide to the customers is representative of what was in the sample.

How can you ensure that liposome morphology is representative and not affected by preparation? We always acquire a set of images from different areas of the grid so it is representative of the sample. We treat samples by “flash freezing” in a liquid ethane bath, where it goes from room temperature to cryogenic temperatures within milliseconds. This ensures that we don’t affect liposome morphology or integrity.

How many particles do you measure? That depends on how many we have on-grid. With sufficient concentration, we analyze at least 1,500 particles to be sure that we get a representative population and statistically relevant data.

Can you measure polyethelene glycol (PEG) layers of liposomes using your electron microscopy techniques? We cannot measure the PEG layer because it is not dense enough to be seen by electron microscopy. The layer is composed mainly of oxygen, hydrogen, and carbon, which doesn’t give you enough contrast against the carbon support.

How do you discriminate between filled and empty vial particle vectors (e.g., packaging of AAV particles)? We produce a radial density profile. Empty particles appear as open circles; filled particles look like discs. By looking at the radius of each particle, we can see that the profile differs depending on whether it is a disc or circle, so we can discriminate between empty and filled.

Is it possible to accurately gauge particles or liposome concentration with your software — and if so, has it been compared with alternate techniques? No, electron microscopy is not suitable to gather quantitative data when it comes to number or concentration of particles. When we talk about quantization, it is a relative analysis such as a ratio between filled versus empty particles.

More Online
The full presentation of this webcast can be found on the BioProcess International website at the link below.

Listen online at www.bioprocessintl.com/Vironova-ATE-2016.

The post Ask the Expert Liposome and Viral Vector Characterization: Use of Electron Microscopy and Image Analysis appeared first on BioProcess International.

BRISTOL-MYERS SQUIBB (WWW.BMS.COM)

Osmolality is a critical attribute for injectable formulations. It is desirable to have products match physiological osmotic conditions. Furthermore, osmolality provides confirmation of soluble content in solution. Preventing injection of hypo- or hyperosmotic solutions is a key element of parenteral formulation development. Additionally, some investigators have explored correlations between injection pain and formulation osmolality, although no significant correlation has yet been observed (1–4).

Osmolality is a valuable in-process test also because it provides a reliable and repeatable value that reflects overall solute content of a formulation. It is particularly useful to detect deviations in compounding when excipients such as stabilizing and tonicity modifying sugars, viscosity modifiers, and buffers are used at relatively high concentrations. Similarly, when the Donnan effect is present during ultrafiltration/diafiltration processes, osmolality is a good indicator of undesired coconcentration of some excipients along with proteins (5, 6).

Based on the above, osmolality is one of the tests included in specifications of parenteral drug products. For accuracy and precision in product-release data, it is crucial to ensure minimized assay variability among different methods, analysts, and sites. Our recent experiences in technology transfer have shown that monitoring and control of assay variability can prove to be particularly challenging when services of third-party manufacturing and research organizations are involved. Based on an investigation on disparities observed during said technology transfers, we feel the need to share our experiences with the broader biopharmaceutical development community.

Osmometers work on two leading principles depending on which soluble content is to be quantified: freezing-point depression and vapor-pressure depression. In both well-established methods, the amount of soluble content is deduced from its effect on colligative properties of a solution. In this study, we investigated the ability of these two working principles to make accurate deductions about formulations that contain high concentrations of soluble polymers (including but not limited to proteins).

Materials and Methods
To compare freezing-point based (FPB) and vapor-pressure based (VPB) osmometers, we used the following variables: IgG1-A, IgG1-B, and PEGylated protein from Bristol-Myers Squibb; and polyethylene glycol (PEG-10K) from Alfa Aesar. IgGs and PEGylated protein concentrations were within the range of 0–25% (w/v) in formulations that contained buffering agents, sugars, amino acids, metal chelators, and surfactants. Proteins and inactive ingredients used in formulations 1–4 were part of BMS proprietary products. For PEGylated protein (with PEG covalently attached) comparator, we prepared a solution containing 75 mg/mL bovine serum albumin (BSA) from SeraCare Life Sciences in deionized water with 0–22% (w/v) concentrations of PEG-10K — listed as formulation 5 herein. For each set of data collected for a given formulation, only the concentrations of proteins and polymers varied in each data group, whereas we kept constant the concentrations of inactive ingredients.

Equation 1: ξ is osmolality (mOsm/kg solution), m_i is molality (moles/kg solution) of i^th solute, v_i is the number of particles formed by the dissociation of i^th solute, and Ф_mi is the molal osmotic coefficient (a measure of ideality in dissociation behavior, which is complete dissociation with no water binding) of the i^th solute.

We followed standard operating and cleaning protocols as described in the operating manuals for both instruments: a Wescor Vapro 5520 (VPB) system from Wescor Inc. and a Model 3320 osmometer (FPB) from Advanced Instruments Inc. Neither instrument displayed errors or alerts during osmolality measurements for evaluated samples.

We calculated theoretical osmolality contributions of dissolved polymers (including proteins) using Equation 1.

Table 1: Tabulated results on comparison of theoretical and experimental osmolality values using two osmometers with different working principles

In accordance with the method described in the instrument manuals, we measured water activity in samples of formulations 2 and 5 at 20 ± 2 °C (Table 1) using an Aqualab 4TEV instrument from Decagon Devices.

Results
Here we compare osmolality measurements performed using VPB and FPB instruments for the same formulations containing different protein and polymer concentrations. In addition, for a reference that is independent of instrument bias, we include theoretically calculated osmolality values for comparison. Table 1 lists osmolality results obtained for formulation buffers and varying concentrations of proteins and polymers in those buffers.

When we analyzed formulation buffer alone — or formulations with low protein concentration (e.g., <50 mg/mL) — the different osmometers provided comparable values within expected instrument-to-instrument variability. However, as Figure 1 shows, osmolality measurements performed on high concentration antibody samples (>150 mg/mL) showed a clear difference according to instrument type (VPB or FPB operating principles).

Figure 1A, 1B: Comparative data showing osmolality as function of protein/polymer solute concentration with overlaid theoretically calculated values (open circles), values measured by an osmometer based on vapor pressure (open triangles), values measured by an osmometer based on freezing point (open squares); (A) IgG1A in formulation 1 and (B) IgG1A in formulation 2

In Figure 1, panels A and B show osmolality data for an IgG1 monoclonal antibody (MAb) in two different formulations. Panel C demonstrates similar data for a second IgG1 MAb. In all three cases, VPB measurements agreed well with theoretically calculated osmolalities of those formulations. We observed an unexpected increase in osmolality with increasing protein concentration when measured with the FPB instrument. Panel D shows osmolality data for a PEGylated protein in a formulation that has an atypically high concentration of sugar (>0.5 M).

Figure 1C, 1D: Comparative data showing osmolality as function of protein/polymer solute concentration with overlaid theoretically calculated values (open circles), values measured by an osmometer based on vapor pressure (open triangles), values measured by an osmometer based on freezing point (open squares); (C) IgG1B in formulation 3 and (D) PEGylated protein in formulation 4

In that case, both VPB and FPB instruments showed notable deviation from theoretical calculations even without protein in the formulation buffer, suggesting that osmotic nonideality is related to low water activity as result of that high sugar concentration. At higher protein concentrations, both instruments showed further deviation from theoretically calculated osmolalities, and it was more notable in data from the FPB instrument.

Figure 1E: Comparative data showing osmolality as function of protein/ polymer solute concentration with overlaid theoretically calculated values (open circles), values measured by an osmometer based on vapor pressure (open triangles), values measured by an osmometer based on freezing point (open squares); (E) PEG-10K in formulation 5

To test whether synthetic polymers also cause instrument bias in osmolality measurements, we collected data on a formulation containing 75 mg/mL BSA and different concentrations of PEG-10K (Figure 1E), which is a highly water soluble polymer with strong hydrogen-bonding ability. This experiment also provided measured osmolalities that were significantly higher than theoretical values with increasing PEG-10K concentrations — again more prominently from the FPB instrument.

Figure 2: Formulations with increasing PEG-10K concentrations (in formulation 5) showed decreasing water activity, whereas increasing antibody concentrations (in formulation 2) did not show the same trend.

In an effort to understand whether decreased water activity was the only contributing factor causing deviations from theoretical values, we measured water activities of formulations 2 (IgG1) and 5 (PEG-10K) as a function of protein and polymer concentrations (Figure 2). With formulation 5, we observed a clear drop in water activity with higher PEG-10K concentration in solution, whereas formulation 2 did not display the same trend. That result suggests the presence of an additional contributing factor to the observed deviations in apparent osmolality with formulation 2: low protein concentration with ingredients that can decrease water activity (e.g., sugars and polymers with high water solubility).

Our results collectively suggest a dual mechanism in which both decreased water activity and antifreeze-like behavior of proteins and polymers in solution contribute to deviations of apparent osmolality from actual solution osmolality calculated based on dissolved solutes. With decreased water activity, that deviation was evident even without the experimental variable (protein or PEG, depending on the formulation tested) and affected both instruments to different degrees. The FPB instrument deviated more than the VPB instrument did.

Icefish (Notothenioidei) larva off the coast of Antarctica UWE KILS (HTTPS://COMMONS.WIKIMEDIA.ORG)

Discussion
Water Activity and Osmotic Nonideality: One hypothesis for explaining the apparent increase in osmolality is the effect of nonideality, which is represented by the molal osmotic coefficient (φ) in Equation 1. Decreased water activity would result in nonideality, which mathematically manifests itself as an increase in osmotic coefficient and consequently an increase in overall osmolality.

Indeed, we did observe a measurable decrease in water activity with increasing polymer concentrations in the case of formulation 5, which had ≤22% (w/v) dissolved PEG-10K. Note that in this scenario, both VPB and FPB instruments measured higher osmolality readings than theoretical values assuming ideal behavior, and the FPB instrument showed a larger deviation. A similarly notable increase occurred in both VPB and FPB readings with increasing solute concentration in the case of formulation 4.

Formulation 4 contains protein-conjugated PEG and an atypically high concentration of sugar, both of which would lower water activity, resulting in osmotic nonideality. That further supports the association of water activity with deviations in osmolality measurements compared with theoretical calculations for some formulations. However, typical biologics formulations such as 1–3 do not have protein-bound solutes in quantities that would significantly lower water activity as a function of protein concentration. Figure 2 shows the lack of trending in water activity as a function of protein concentration in solution (formulation 2), with a strong decrease in the case of a PEG solution (formulation 5). So water activity alone fails to sufficiently explain the observed differences between values measured by VPB and FPB instruments. Similarly strong discrepancies between theoretical and experimental values of osmolality with PEG present are discussed in reports by Sandell and Goring (7) and by Elworthy and Florence (8).

Antifreeze Effect: Polyols such as ethylene glycol and propylene glycol are widely used as antifreeze materials because of their ability to mimic and replace hydrogen bonding between water molecules. In doing so, they perturb the ability of water molecules to achieve an ordered state that can crystallize into ice upon nucleation (9). Similar hydrogen bonding ability is observed in glycoproteins from fish that inhabit deep seas in polar regions, where such proteins prevent formation of ice crystals in the bodies of such fish at temperatures well below the freezing point of water (10, 11). Similar proteins also have been found in other organisms such as certain insects and cold-tolerant plants (12, 13). These proteins are especially evolved to provide protection against ice crystals; therefore, they do not have to be present at very high concentrations to be effective. However the involvement of hydrogen bonding in their mechanism of action suggests that any solute capable of strong hydrogen bonding will have varying degrees of antifreeze-like properties.

Many amino acid side chains in proteins can hydrogen bond with water. Additionally, a well-known property of proteins is hydrogen bonding ability of the peptide backbone itself. Sugar chains on glycosylated proteins represent a third potential source of hydrogen bonding moiety. Considering the availability of numerous functional groups that can replace the hydrogen bonding between water molecules, it is easily conceivable that proteins like other hydrogen-bonding solutes would display some antifreeze-like properties. In doing so, they would hinder the packing of water structure into an ordered cage of hydrogen bonds for crystallization. That effect would be in addition to the freezing-point depression in its classical meaning, which would originate from protein’s presence as a solute in the aqueous environment.

Because instruments based on freezing-point depression deduce osmolality solely based on the classical meaning of colligative properties, such an instrument would misinterpret the antifreeze-containing sample as a solution with much higher concentration of solute. That leads to an artificially high osmolality value. However, proteins that display antifreeze-like properties are not expected to have equally strong effects on increasing vapor pressure because their mechanism of action involves hindering lattice-like packing of water molecules more than the strength of their own interaction with water. That is consistent with our observations that VPB instruments provided more reliable alternatives to FPB instruments, particularly for high-concentration protein formulations (e.g., subcutaneous products).

Cryoconcentration Artifacts During FPB Measurements: Protein formulations are known to undergo cryoconcentration, which is freezing-triggered loss (during water crystallization) of content uniformity in bulk formulation. That creates gradients of solute concentrations and potentially phase separation of one or more ingredients in a formulation. Any phenomenon that affects content uniformity also has potential to change accuracy and precision of analytical methods through multiple mechanisms. Thus, FPB instruments have the added disadvantage compared with VPB instruments because of possible artifacts of freezing that can take place during measurements (14). Those can lead to observations of nonrepresentative physicochemical properties caused by a lack of content uniformity as well as nucleation of ice-crystal formation by particles generated during freezing triggered phase separation. Bhatnager et al. have reviewed a more comprehensive list of freezing-triggered phenomena and detailed their effects in protein formulations (15).

Additional Considerations: The possible causes of discrepancies between VPB and FPB osmometer results discussed herein do not constitute an exhaustive list of cautionary notes for these techniques. The simplest techniques with minimal sample preparation hold an unusual risk for oversight of their subtle complexities, partly because no deep expertise is necessary to run such tests. Sweeney and Beuchat have listed the assumptions and mathematical simplifications that are part of osmolality measurements and analysis (16). Inaccuracies can occur when instrumental limitations are poorly understood by analysts.

A Cautionary Tale
Despite published literature referenced herein, suboptimal practices in generation and interpretation of osmolality data are of continued concern in this field. Cautionary reports are scattered across a number of journals that serve diverse audiences, not necessarily targeting pharmaceutical research and development. Based on our observations, some conclusions can be drawn:

• VPB osmometers appear to provide a more reliable alternative to FPB osmometers for high-concentration  protein formulations and those that contain high concentrations of ingredients that can decrease water activity (e.g., sugars and highly water-soluble polymers). Antifreeze effects are protein/formulation dependent and will manifest at different concentrations for different proteins and formulations. At low concentrations of polymers, proteins, and sugars, both instrument types will provide satisfactory results.
• When working with third-party research/manufacturing organizations or performing technology transfer between sites (and setting specifications), it is beneficial for analytical method and process developers to consider the instrument types used for osmolality measurement. Misalignment between theoretical and experimental osmolality results should not be attributed fully to nonideality and/or instrument bias. For example, osmolality is a useful tool to highlight the presence of Donnan effects in ultrafiltration/diafiltration operations. Investigations of discrepancies should include quantification of excipients and comparison of their levels with target concentrations.

Acknowledgments We thank Miron Ludzinski, who provided formulation 5 for testing.

Conflict of Interest We have no personal financial or nonfinancial conflicts of interest in submission and publication of this manuscript.

References
1 Doenicke A, et al. Osmolalities of Propylene Glycol-Containing Drug Formulations for Parenteral Use: Should Propylene Glycol Be Used As a Solvent? Anesth. Analg. 75(3) 1992: 431–435.

2 Klement W, Arndt JO. Pain on Injection of Propofol: Effects of Concentration and Diluent. Br. J. Anaesth. 67(3) 1991: 281–284.

3 Larsen B, et al. Less Pain on Injection By a New Formulation of Propofol? A Comparison with Propofol LCT. Anaesthesist. 50(11) 2001: 842–845; doi:10.1007/s00101-001-0234-0.

4 Sim JY, et al. Pain on Injection with Microemulsion Propofol. Br. J. Clin. Pharmacol. 67(3) 2009: 316–325; doi:10.1111/j.1365-2125.2008.03358.x.

5 Stoner MR, et al. Protein–Solute Interactions Affect the Outcome of Ultrafiltration/Diafiltration Operations. J. Pharm. Sci. 93(9) 2004: 2332–2342.

6 Steele A, Arias J. Accounting for the Donnan Effect in Diafiltration Optimization for High-Concentration UFDF Applications. BioProcess Int. 12(1) 2014: 50–54.

7 Sandell LS, Goring DAI. Correlation Between the Temperature Dependence of Apparent Specific Volume and the Conformation of Oligomeric Propylene Glycols in Aqueous Solution. J. Polymer Sci. B Phys. 9(1) 1971: 115–126.

8 Elworthy PH, Florence AT. Chemistry of Non-Ionic Detergents Part X: Activity Coefficients of Polyoxyethylene Glycols and Alkyl Polyoxyethylene Glycol Ethers in Aqueous Solution. Kolloid-Zeitschrift und Zeitschrift für Polymere 208(2) 1966: 157–162.

9 Mallajosyula SS, Vanommeslaeghe K, MacKerell AD. Perturbation of Long-Range Water Dynamics As the Mechanism for the Antifreeze Activity of Antifreeze Glycoprotein. J. Phys. Chem. B 118(40) 2014: 11696–11706.

10 DeVries AL, Komatsu SK, Feeney RE. Chemical and Physical Properties of Freezing Point-Depressing Glycoproteins from Antarctic Fishes. J. Biol. Chem. 245(11) 1970: 2901–2908.

11 Komatsu S, DeVries AL, Feeney RE. Studies of the Structure of Freezing Point– Depressing Glycoproteins from an Antarctic Fish. J. Biol. Chem. 10(245) 1970: 2909–2913.

12 Graham LA, et al. Hyperactive Antifreeze Protein from Beetles. Nature 388(6644) 1997: 727–728.

13 Griffith M, Yaish MW. Antifreeze Proteins in Overwintering Plants: A Tale of Two Activities. Trends Plant Sci. 9(8) 2004: 399–405.

14 Winzor DJ. Reappraisal of Disparities Between Osmolality Estimates By Freezing Point Depression and Vapor Pressure Deficit Methods. Biophys. Chem. 107(3) 2004: 317–323; doi:10.1016/j.bpc.2003.11.010.

15 Bhatnagar BS, Bogner RH, Pikal MJ. Protein Stability During Freezing: Separation of Stresses and Mechanisms of Protein Stabilization. Pharm. Dev. Technol. 12(5) 2007: 505–523.

16 Sweeney TE, Beuchat CA. Limitations of Methods of Osmometry: Measuring the Osmolality of Biological Fluids. Am. J. Physiol. 264 (3, part 2) 1993: R469–R480.

Corresponding author Erinc Sahin and Vishal Nashine are senior research investigators; Aastha Puri is a research scientist; Mehrnaz Khossravi is associate director; and Rajesh Gandhi is group director of drug product science and technology at Bristol-Myers Squibb, New Brunswick, NJ 08903; erinc.sahin@bms.com.

The post Osmolality Measurements for High-Concentration Protein–Polymer Solutions: Variation Based on Working Principles of Osmometers appeared first on BioProcess International.

Figure 1: Absorbance Fourier-transform infrared (FT-IR) spectra of different test articles made of polypropylene

The presence of visible foreign particulate matter is considered a critical defect in parenteral products and one of the main reasons they can be recalled (1). Foreign particles present during any stage of manufacturing are considered to be contaminants and can impose a risk to the control of the manufacturing processes (2). For those reasons, particle contamination arising in any manufacturing step initiates a nonconformance or out-of-specification observation. That requires an investigation to identify root cause so as to mitigate the risk of repetition.

Investigations begin with particle identification. Appropriate analytical technologies must be used to determine particle composition. Elemental and chemical composition analyses and physical appearance characterization are commonly referenced methods (3). Whereas particle identification is a critical step in such investigations, finding the root cause of particle contamination can be challenging and time-consuming.

To improve our investigative effectiveness on root-cause determination, we have taken a proactive approach by constructing analytical libraries of raw materials, product-contact materials, and consumables that could generate particles and therefore contaminate a biomanufacturing process. Test articles used to build each library include filter assemblies, tubing, hoses, O rings, gaskets, vial stoppers, gowning supplies, packaging materials, and other miscellaneous items. To date, the total number of test articles is 228, and the library is updated regularly with additional materials.

Samples have been analyzed by Fourier-transform infrared (FT-IR) spectroscopy and in some cases Raman spectroscopy. FT-IR and Raman are both vibrational techniques that characterize the chemical structure of molecules. Generally, polar functional groups generate strong IR absorption whereas nonpolar functional groups induce strong Raman spectra (4). Raman spectroscopy is particularly beneficial for characterizing inorganic chemicals with characteristic vibrational energy shifts appearing in a spectral range <800 cm^–1 (5). That low spectral range can be missed by FT-IR, depending on the use of different sample compartment accessories or microscopes.

Samples were also analyzed by scanning electron microscopy with energy-dispersive X-ray spectrometry (SEM-EDS). It is a well-established technology for characterizing size, morphology, and elemental composition of test articles. In principle, EDS differentiates unique X-ray emission of elements from beryllium after they are exposed to a focused beam of electrons, and it provides for qualitative or quantitative elemental analyses (6).

The primary objective of our study was to build analytical libraries of raw materials, product-contact materials, and consumables using analytical technologies commonly applied for particle investigations. We found that it is essential to use complementary technologies to achieve comprehensive characterization of foreign particles.

Table 1: Test articles used to build the analytical library

Materials and Methods
Consumables, product-contact materials, and raw materials (228 samples in total) were collected from three AstraZeneca manufacturing sites. We categorized those test articles into 10 groups based on their origins (Table 1). Each group contained similar products, either of differing sizes or supplied from different vendors. Filter and single-use bag assemblies comprised various components such as connectors, housings, and hoses. We also analyzed multicomponent chemicals such as cell culture media, feeds, and buffer solutions. Miscellaneous items included cleaning wipes, bench pads, autoclave bags, and a range of single-use items that we determined not to belong in any other groups listed in Table 1.

For analysis, we cut up bulk test articles into small pieces (several millimeters) and dried liquid chemicals using a rotary evaporator. We used chemicals that came in powder or crystal form directly for testing.

To date, we have analyzed 228 test articles by SEM-EDS using a Hitachi TM3030 scanning electron microscope and Bruker Quantax 70 energy-dispersive X-ray spectrometer. To do so, we affixed small pieces of test articles to carbon tabs (part number 77827 from Electron Microscopy Sciences), placing them on aluminum stubs and then inside the instrument under low vacuum. SEM images were acquired on EDX mode at low magnification. EDS analysis followed using the Quantax 70, which enables us to measure elements down to boron. We took EDS measurements for a minimum of 200 seconds.

Eight articles we had analyzed by SEM-EDS were not suitable for FT-IR analysis. But we analyzed 220 of 228 test articles using a Spectrum 400 FT-IR system from PerkinElmer with a universal attenuated total reflection (ATR) accessory. We placed test articles on a diamond top plate of the universal ATR and adjusted the pressure on them to ensure good contact between the samples and the plate. Spectra were acquired through eight accumulated scans within a range of 4,000–650 cm^–1 at 4-cm^–1 resolution. We also applied theoretical atmospheric correction of H₂O and CO₂ during spectral acquisition and used Spectrum 10 software (PerkinElmer) to acquire and process the IR spectra for analyses and annotation.

We characterized eight samples using a Raman DXR microscope from Thermo Scientific with a 532-nm, 10-mW laser. After determining the optimal acquisition time for each sample to increase the signal-to-noise ratio, we exposed the samples to the laser for a minute before Raman acquisition to photobleach the background fluorescence. And we used OMNIC software (Thermo Scientific) to acquire, analyze, and annotate the resulting Raman spectra. Finally, we identified chemicals based on our FT-IR and Raman spectra using a commercially available KnowItAll Informatics spectral library from Bio-Rad Laboratories.

Results and Discussion
We prepared FT-IR and EDS libraries with the materials listed in Table 1, converting the test articles’ FT-IR spectra to a searchable library using Spectrum 10 software and tabulating our EDS results in a Microsoft Excel file for manual searching. Then we further analyzed our FT-IR spectra and EDS results to understand the diversity of information built into them and the benefits of using multiple analytical technologies for particle investigations.

FT-IR analyses indicated that many test articles were made from the same polymer families. For example, 38 test articles were composed primarily of polyethylene, and 30 test articles were made of polypropylene. Those samples came from a wide range of materials: containers, packaging materials, single-use bag and filter assemblies, gowning supplies, and so on. We also found other redundant polymers in the library, including polydimethylsiloxane, polyamide, polyester, polyvinyl chloride, polycarbonate, and polysulfone. These observations suggest that applying only FT-IR to identify contaminant particles (especially those made of ubiquitous polymers) can be challenging because the contaminants can originate from many potential sources.

SEM-EDS measures the relative weight percentage of different elements. During this study, we isolated replicate samples from a single bulk material and analyzed them by EDS. Although the same elements were identified across replicates, the reported weight percentage of elements showed some variability (data not shown). The probable reason for that variability is the small area that EDS detectors sample (micrometer square or smaller) for each measurement, which may not represent the average composition of a given test sample. For that reason, we believe that the EDS library should be used for searching overall elemental profiles rather than absolute numerical agreement. In our current study, we present EDS results in relative quantities, reporting elements present at a minimum of 0.1% (w/w), which is a limit of detection (LoD) for the Quantax 70 EDS determined at our laboratory.

Adding SEM-EDS analysis to FT-IR analysis can increase the probability of identifying differences among materials with similar chemical composition. An example is polypropylene, a common polymer found in the library. Figure 1 presents
the FT-IR spectra of that derived from seven different test articles. All spectra were indistinguishable from one another, and all presented IR bands characteristic to polypropylene such as stretching modes of methyl and methylene groups around 2,950, 2,920, 2,870, and 2,840 cm^–1 and bending modes at 1,460 and 1,380 cm^–1 (7). Overall, those FT-IR spectra agree with the polypropylene spectrum in the KnowItAll commercial library (data not shown).

Table 2: EDS elemental profiles of different test articles made of polypropylene; ND = not detected, SUB = single-use bag, and FA = filter assembly

However, SEM-EDS analyses of the polypropylene samples revealed differences in their elemental composition. Table 2 summarizes the elemental profiles of polypropylene samples from Figure 1. The chemical formula of polypropylene is (C₃H₆)_n. Because the EDS we used detects elements from boron up, carbon is the only element derived from polypropylene to generate an EDS signal. Oxygen and different sets of trace elements were observed in some samples, probably indicative of additives used to improve thermal, mechanical, and/or chemical stabilities of polypropylene, depending on the intended purposes of polymer products. Antioxidant phenolic compounds, calcium stearate, titanium oxide, and compounds for surface fluorination are just a few chemicals that can be used to mold polypropylene (8–10). Generally, EDS can identify additives or impurities present at trace quantities that may not be IR-active or are present in insufficient quantities to yield IR bands. So the EDS information can be crucial for identifying particles and their sources.

Table 3: EDS elemental profiles of stoppers; ND = not detected

EDS is also a useful adjunct to IR spectral analyses of test articles processed through the KnowItAll database. It runs an IR spectral search against a built-in library of >200,000 spectra. The program generates numerous search results based on composite spectral similarities, often suggesting the presence of unexpected chemical components. EDS results showing the elemental compositions of test articles allow analysts to eliminate nonrelevant IR spectral search hits, making chemical identification more likely.

Figure 2: Energy-dispersive X-ray spectroscopy (EDS) spectra of stoppers A (top), B (middle), and C (bottom); all spectra are shown at the same scale for both the zoom and full (insets) views.

Raman spectroscopy is another useful technology for characterizing particle samples. Complementary to FT-IR, it is a vibrational spectroscopy that can reveal IR-inactive vibrational modes of chemical bonds. Raman may be needed for comprehensive characterization of heterogeneous samples. Material from drug-product vial stoppers is a good example of a complex material that requires all three analytical technologies.

We analyzed stoppers A, B, and C with both EDS and FT-IR for library preparation. Triplicate or quadruplicate samples came from each stopper, and their EDS-generated weight percentage of elements were averaged and reported in nonnumerical scales (Table 3). Each stopper presented a distinctive elemental profile. Overall, stoppers A and C presented similar elemental profiles, but stopper A contained a trace amount of chlorine (unique compared with other two stoppers). Stopper B consisted of primarily carbon and oxygen, with trace amounts of fluorine, sulfur, and aluminum. Figure 2 presents the corresponding EDS spectra of all three stoppers.

Figure 3: Absorbance FT-IR spectra of stoppers; IR bands characteristic to poly(isobutylene), silicone, and magnesium silicate all are labeled in wavenumbers on stopper A spectrum; poly(isobutylene) bands are labeled with * on stopper B and C; silicone IR bands are labeled with ** on stoppers B and C; and magnesium silicate IR bands are labeled with *** on stopper C.

Figure 3 presents FT-IR spectra of the three stoppers within the range 2,000–650 cm^–1. All stoppers presented IR bands characteristic to poly(isobutylene) at 1,470, 1,388, 1,366, and 1,231 cm^–1. Doublet bands at 950 and 923 cm^–1 observed in stopper B also corresponded to poly(isobutylene). Both peaks appeared as shoulder peaks from stoppers A and C. Strong IR bands at 1,010 and 670 cm^–1 in stoppers A and C corresponded to magnesium silicate, which is probably used as a filler. Instead of magnesium silicate, stopper B appeared to include silicone rubber, based on IR bands observed at 1,261, 1,105, 1,020, and 800 cm^–1. Those silicone-related peaks were also present in stoppers A and C.

Figure 4: Absorbance Raman spectra of stoppers; Raman shifts characteristic to poly(isobutylene) and anatase are labeled in wavenumbers on stopper A spectrum; polyisobuytlene bands are labeled with * on stoppers B and C; anatase bands are labeled with ** on stopper B; and wavenumbers of Raman shifts corresponding to polyethylene and rutile are recorded on stopper B and stopper C, respectively. The peak at 145 cm^–1 on stopper C is shown truncated in Raman intensity at this scale.

So we used Raman spectroscopy to analyze the stoppers. Figure 4 shows the resulting spectra in the range of 2,000–50 cm^–1. Stoppers A, B, and C all generated Raman peaks corresponding to poly(isobutylene) around 1,442, 1,227, 924, 853, and 717 cm^–1. Stopper B displayed additional Raman bands at 1,295, 1,128, and 1,062 cm^–1, suggested by a KnowItAll Raman spectral search to be associated with methylene twisting and C–C bond stretching modes, which probably come from polyethylene (11). In addition to the organic chemicals, Raman spectroscopy also detected the presence of titanium oxide in all stoppers. Furthermore, it could distinguish the different crystal structures of titanium oxide (e.g., anatase and rutile). Raman shifts of rutile showed up in stoppers A and B at 611, 442, and 228 cm^–1. The intense band at 145 cm^–1 in stopper C is a major signature peak of anatase, and other anatase peaks at 637, 514, and 396 cm^–1 also showed up for stopper C. Silicone-related bands were not distinguished by Raman, however, probably due to its inherently weak Raman signal compared with FT-IR as well as interference of other Raman shifts around 710 and 490 cm^–1 where silicone-related Raman bands usually appear.

Application of all three analytical technologies gave us comprehensive stopper characterization. Stopper A consisted of poly(isobutylene), magnesium silicate, a silicone-based material, and rutile. Stopper B was made of poly(isobutylene), polyethylene, silicone rubber, and rutile. And stopper C contained poly(isobutylene), magnesium silicate, silicone, and anatase.

Work in Progress
We prepared FT-IR and EDS libraries of raw materials, product-contact materials, and consumables to facilitate investigations of foreign-particle contaminations in biomanufacturing processes. Analysis of these libraries indicated that combining EDS with FT-IR distinguishes subtle differences in trace elements present in test articles that are made of similar chemicals. Trace elements might be introduced intentionally as additives for raw-material manufacturing, or they may be impurities associated with specific raw materials. Adding Raman spectroscopy — especially for analysis of heterogeneous test articles — reveals chemical components that FT-IR cannot identify. So the combined application of FT-IR, SEM-EDS, and Raman spectroscopy for particle investigations helps ensure full particle characterization.

We are updating our libraries with critical materials that directly contact biological products throughout their manufacturing processes. We are expanding our studies to include multiple lots of materials for detecting lot-to-lot variability as well as profiling particulate contaminants analyzed in our investigations.

Acknowledgments
We acknowledge Dr. Kun Yao, Dr. Kuruppu Dharmasiri, and Christopher Shaw for helpful discussion and support; and Dr. James D’Alessio and Eric Beard for critical review.

References
1 Bukofzer S, et al. Industry Perspective on the Medical Risk of Visible Particles in Injectable Drug Products. PDA J. Pharm. Sci. Technol. 69(1) 2015: 123–139; doi:10.5731/pdajpst.2015.01037.

2 Borchert SJ, et al. Particulate Matter in Parenteral Products: A Review. PDA J. Pharm. Sci. Technol. 40(5) 1986: 212–241.

3 Wolfe J, Smith K. Forensic in QbD: Addressing Foreign Particulate Matter Investigation. Am. Pharmaceut. Rev. 2 September 2014; www.americanpharmaceuticalreview.com/Featured-Articles/167460-Forensics-in-QbD-Addressing-Foreign-Particulate-Matter-Investigations.

4 Wartewig S, Neubert RH. Pharmaceutical Applications of Mid-IR and Raman –Spectroscopy. Adv. Drug Deliv. Rev. 57(8) 2005: 1144–1170; doi:10.1016/j.addr.2005.01.022.

5 Hibben JH. Raman Spectra in Inorganic Chemistry. Chem. Rev. 13(3) 1933: 345–478.

6 Russ JC. Fundamentals of Energy Dispersive X-Ray Analysis. Butterworths: London, UK, 1984.

7 Andreassen E. Infrared and Raman Spectroscopy of Polypropylene. Polymer Sci. Technol. 2, 1999: 320–328.

8 Van Krevelen DW, Nijenhuis KT. Properties of Polymers. Elsevier Science: Amsterdam, The Netherlands, 2009.

9 du Toit FJ, Sanderson RD. Surface Fluorination of Polypropylene: 1. Characterization of Surface Property. J. Fluorine Chem. 98(2) 1999: 107–114.

10 Esthappan SK, et al. Effect of Titanium Dioxide on the Thermal Ageing of Polypropylene. Polymer Degrad. Stabil. 97(4) 2012: 615–620.

11 Lin-Vien D, et al. The Handbook of Infrared and Raman Characteristic Frequencies of Organic Molecules. Academic Press: Cambridge, MA, 1991.

Corresponding author Junghwa Kim, PhD is a scientist II, Derek Schildt is a scientist I, and Dipali Patel is an associate scientist II in the manufacturing science and technology biophysical laboratory at Frederick Manufacturing Center, AstraZeneca, 633 Research Court, Frederick, MD 21703; 1-301-228-5000; kimjun@medimmune.com, schildtd@medimmune.com, pateldip@medimmune.com. Simon Chen was a summer intern with them and is currently a student at the School of Public Health in the University of Maryland in College Park, MD.

The post Investigation of Foreign-Particle Contamination: Practical Application of FT-IR, Raman, and SEM-EDS Technologies appeared first on BioProcess International.

WWW.PHOTOS.COM

Exposure to solid–liquid and air–water interfaces during production, freezing and thawing, shipment and storage of protein therapeutics may be a contributing factor in their degradation (e.g., aggregation, fragmentation) (1, 2). Surface exposure, particularly during manufacturing processes, often is accompanied by various degrees and durations of shear stresses originating from fluid flow and acting on proteins at interfaces. The magnitude and duration of shear rates depends on velocity gradients within each solution and varies significantly among manufacturing steps. On the low end, a shear rate of 50 s^–1 (inverse seconds or Hertz) is applied to biotherapeutics during mixing processes typically lasting from minutes to hours. On the high end of the range, shear rates of up to tens of thousands of Hertz arise during filling (up to a million during high-pressure homogenization), but those usually are limited in duration to mere seconds (3). Additionally, proteins may be subjected to high shear rates when injected through thin needles (4).

No consensus yet has been reached on whether surface interaction, shear stress, a combination of those, or other events (e.g., cavitation) are causative for frequently observed protein aggregation (3, 5–7). Although Bee and colleagues found that the shear rates of manufacturing and injection are insufficient to induce protein aggregation or denaturation, Biddlecombe and colleagues have expressed an opposing opinion (4, 6). Using a rotating-disk device as a shear stress environment, they found that a shear rate of 30,000 s^–1 can cause aggregation of an IgG4 as assessed by size-exclusion chromatography (SEC). However, the extensive solid–water interface could be a major contributor to protein degradation in their experimental set-up.

Previously, Perevozchikova and colleagues showed that a monoclonal antibody (MAb 1) in a citrate formulation adsorbed irreversibly to solid silicon oxide (SiOx) surfaces (8). Desorption induced by gentle buffer rinses over the SiOx layer resulted in formation of subvisible, micron-sized protein particles; however, no smaller oligomers were observed using multiple techniques.

Here, we extend this area of study by introducing high shear stress and evaluating the stability of the above-mentioned formulation (MAb 1 in citrate buffer) exposed to solid–water interfaces in the presence of a 20,000-s^–1 shear rate, which resembles the maximum shear stresses encountered in biomanufacturing. We compare the degradation of MAb 1 in a citrate formulation to that of MAb 1 and MAb 2 in alternative histidine formulations to determine whether a common aggregation pathway exists for shear/surface-stressed antibodies. To test whether higher shear rates but shorter surface exposures would have the same detrimental effect on protein quality, we also subject those formulations to shear rates typical of injections.

Materials
We used three MAb formulations at 20 mg/mL in this study: MAb 1 in a citrate-based formulation and a histidine-based formulation and MAb 2 in a histidine-based formulation. To mimic the effects of manufacturing stress on biotherapeutic stability, we subjected both MAbs to the shear rate of 20,000 s^–1 for 300 and 1,800 seconds, respectively. Both periods significantly exceed the typical durations of milliseconds to several seconds when proteins experience 20,000 s^–1, and we chose them to demonstrate a worst-case, forced-degradation scenario.

We created a controlled shear-stress environment using a rotation rheometer set-up as previously described by Bee and colleagues (3). Stress is induced by placing 0.3 mL of a MAb sample onto the lower plate of the parallel plate (PP) measuring system (0.1-mm gap) of an Anton Paar Physica M301 rheometer and subjecting it to rotational shear for a specified period. We repeated this procedure three times to collect an amount of stressed sample sufficient for our planned analytical testing.

To control for degradation induced by surface interactions alone, we placed an aliquot of the protein formulation on the PP50 unit between nonrotating plates for a selected period (300 or 1,800 seconds). To estimate the contribution of nonproteinaceous particles leached from the PP surface to the overall microflow imaging (MFI) counts observed in stressed MAb formulations, we applied an additional citrate buffer sample to the rheometer at 20,000 s^–1 for 1,800 seconds.

For the high–shear-rate and short–surface-exposure stress conditions, we subjected MAb 1 and MAb 2 molecules to shear rates of ~100,000 s^–1 and ~300,000 s^–1 by injecting them through a 27-gauge needle at 0.0625 and 0.2 mL/s, respectively. To amplify the amount of stress applied to these samples, we injected each formulation three times. A 3-mL MAb formulation sample in a 10-mL Becton Dickinson (BD) syringe with a 27-gauge needle was placed on a syringe-pump (Harvard Apparatus) set up with one of the two specified flow rates.

Table 1: Samples created and their abbreviations in graphs and tables

To evaluate whether any particles were contributed by the surface of the syringe itself, we poured a control sample directly between the inner surfaces of two 10-mL syringe barrels for a total of three times. Table 1 describes samples created during this study. Exposed MAb samples were analyzed by visual inspection, SEC with multiple-angle light scattering (SEC-MALS), dynamic light scattering (DLS), and MFI. We analyzed the citrate buffer sample with MFI alone.

Methods
Visual Inspection: We evaluated sample appearance by visually examining the color, clarity, and particulate content of each test article. Colors were evaluated by comparison with a certified color standard prepared to a prespecified range of intensities in accordance with the European Pharmacopoeia. Clarity was evaluated by comparison with water. We also visually examined our samples for the presence of particulate matter.

SEC-MALS: To determine the molecular-weight distribution of our biopharmaceuticals, we used high-performance SEC-MALS with UV and refractive index (RI) concentration detectors. Each species was separated on a 30 cm × 7.8 mm TSK-GEL G3000 SWXL analytical column (Tosoh Biosciences). For light-scattering measurements, we used an 18-angle Dawn Heleos II laser light scattering unit (Wyatt Technology) equipped with a QELS probe at detector 16 (140.7°). We measured sample concentration by UV detection at 280 nm.

Using a first-degree fit of the Zimm formalism in Astra 6 software (Wyatt Technology) and the standard protein refractive index increment (dn/dc) value of 0.185, we calculated molecular weight. Light-scattering detectors were normalized to the 90° detector in the mobile phase using isotropic scattering of a bovine serum albumin (BSA) monomer before all calculations.

Table 2: Size-exclusion chromatography with multiangle light scattering (SEC-MALS) results on samples exposed to lower shear and long surface interaction conditions

DLS: Formation of aggregates in the nanometer-scale size range was followed by DLS using the Mobius Wyatt instrument in QELS mode. We placed undiluted 55-µL samples into disposable cuvettes and analyzed them with a DLS acquisition time of two seconds, collecting a total of 10 DLS acquisitions per measurement. Then we obtained an average of three measurements and calculated the standard deviation for adding to the graphs.

Data were analyzed using a 0.5-nm minimum and 1,000-nm maximum species size. Monomer peak 1 was assumed to represent monomer species because it ranged to 10 nm, peak 2 would represent smaller oligomers (tetramer to decamer), and peak 3 would be larger species (hundreds of nanometers). We measured the hydrodynamic radius, percent polydispersity, and percentages by intensity and mass of each defined peak.

MFI is an image-based method that measures particles as they flow through the detection zone in a flow cell. We used a Protein Simple MFI DPA4100 instrument for subvisible particle analysis, operating the instrument at low-magnification mode (5×) with a 400-µm flow cell. For each analysis, we set the sample run flow rate at 0.22 mL/min and analyzed 0.5-mL samples in triplicate. We checked flow-cell cleanliness with a blank run using water for injection (WFI) before each measurement. Particle concentrations (number of particles/mL) are reported in different size ranges based on their measured equivalent circular diameter.

Figure 1: Dynamic light-scattering (DLS) experiments evaluate the aggregation propensity of MAbs subjected to different durations of shear stress and extents of surface exposure; (top) change in molecular hydrodynamic radii; (middle) percent polydispersity with increasing exposure to shear stress; (bottom) percent by mass of monomer peak reduced for sample exposures to the large surface area but relatively low shear stress. Error bars are calculated from running experiments in triplicate on each sample.

Results
Visual inspection indicated different initial general properties of tested formulations. MAb 1 was a hazy, colorless solution in both formulations; whereas MAb 2 represented a slightly yellow (BY6) liquid. Nevertheless, we found that all collected samples retained their original visual characteristics and remained particle free, signifying that no visible aggregates were formed by exposure to the shear stresses.

SEC-MALS Analysis: We used SEC-MALS to probe low–molecular-weight (MW) oligomers formed as a response to prolonged surface interactions in the presence of a 20,000 s^–1 shear rate. Data obtained revealed no monomer loss in any of the three formulations (Table 2). We observed the same results for MAbs exposed to different injection shear rates (data not shown).

DLS: We investigated the formation of nanometer-scale aggregates using DLS. Figure 1(top) shows that no detectable changes in hydrodynamic radius (Rh) of peak 1 species (Rh cut-offs of 0.1–10 nm) resulted from low shear with long surface and high shear with short surface exposure. Similarly, we observed no trend of increasing polydispersity as a function of increasing shear stress in this study (Figure 1, middle). Both observations are in agreement with the SEC-MALS data, suggesting that formation of small aggregates is not associated with stresses induced in all conditions.

However, our assessment of the percent by mass of peak 1 (Figure 1, bottom) indicated a decrease of monomer contribution to the overall intensity in samples exposed to the lower 20,000 s^–1 shear rate and longest surface interaction. Further examination of the DLS profile revealed the remaining species of the observed DLS signal to be aggregates sized in hundreds of nanometers. Because that size range is almost out of DLS detection capabilities, we analyzed the stressed and control samples using MFI.

Figure 2: Microflow imaging (MFI) measurements demonstrate the effect of low shear with high surface exposure and of high shear alone on particle formation in three formulations: (top) MAb 1 in citrate buffer, (middle) MAb 1 in histidine buffer, and (bottom) MAb 2 in histidine buffer. Error bars are calculated from triplicate measurements. Sample abbreviations are as described in Table 1 and Figure 1 (with CitrBuffer1800s for buffer alone subjected to 20,000 s^–1 shear for 1,800 s). Control samples exposed to no stress; ECD = estimated circular diameter.

MFI: We used MFI to evaluate the propensity for subvisible particle formation as a consequence of surface interaction and/or shear stress. To control for particles possibly leaching from the surface of the rheometer during plate rotations, we subjected citrate buffer to the shear rate of 20,000 s^–1 for 1,800 seconds. Figure 2(top) shows an increase in particles of all size ranges resulting from surface contact for both 300-s and 1,800-s periods (for MAb 1 in citrate buffer).

The most significant increase came with the 20,000 s^–1 shear rate for 1,800 seconds. “CitrBuff1800s” demonstrates that ~6,000 particles leached from the surface of the PP set-up during rotation (compared with 98,000 particles in a protein solution). Those results suggest that the observed increase of particles throughout all size ranges should be attributed to formation of proteinaceous species and considered to be a consequence of shear stress and surface interaction on protein stability.

But the MAb 1 citrate sample exposed to syringe injection (high shear with low surface exposure) demonstrated a negligible increase in the number of particles, with most of those originating from silicon oil droplets in the solution (their aspect ratio being ≥0.85). Figures 2(middle) and 2(bottom) show a similar observation for both MAb 1 and MAb 2 in histidine buffers: a measurable increase in particle count related to the low shear and surface interaction of the PP, but no effect of high shear on the observed particle formation after triple needle injection.

Figure 3 shows a screening for particle-formation propensity of tested formulations and antibodies caused by exposure of protein molecules to 20,000 s^–1 shear rates for 1,800 seconds. Throughout all size ranges — except for particles >50 µm, which are too few to reliably compare the formulations — the MAb 1 histidine formulation appears to be the most stable against shear/surface-induced degradation. MAb 1 in citrate is the most susceptible to stress-induced aggregate formation. Comparing formulated unstressed samples would yield no ranking insights: All controls show the comparable number of particles between them.

Figure 3: Screening MAb solutions for their propensity for aggregation induced by shear stress and surface interaction; three formulations were compared based on the extent of particle formation after exposure to 20,000 s^–1 shear stress rate for 1,800 seconds. Straight and dotted lines represent stressed (20K1800s, samples exposed to 20,000 s^–1 shear rate for 1,800 seconds) and control (Cntr, unexposed) samples, respectively. The y axis shows the number of particles formed within each size range measured as estimated circular diameter (ECD) — red (2–10 µm), blue (>10 µm), green (>25 µm), and yellow (>50 µm).

Discussion
Expanding on the previous work of MAb 1 adsorption to a SiOx surface (8), we investigated whether additional stress would alter or exacerbate the degradation pathway of the molecule by exposing the same antibody to that solid interface both with and without shear stress. We have found that for MAb 1 in a citrate-based formulation, aggregates formed predominantly in the subvisible particle-size range, as detected by DLS (particle sizes in the hundreds of nanometers) and MFI (2–100 µm particle sizes). No visible aggregates were detected.

We also observed no signs of degradation using different analytical techniques for MAb 1 injected three times through a 27-gauge needle. Because no aggregate formation was detected following the high shear stress and short surface exposure of injection, we believe that a likely explanation for the observed protein instability is interaction of the protein with the solid surface. Protein adsorption to the surface could lead to partial unfolding of the molecular structure followed by desorption of the resulting partially unfolded species, which would then nucleate aggregation. Alternatively (or additionally), surface aggregates could slough off to become detectable in solution.

We find it interesting that exposing MAb 1 in a citrate solution to SiOx surfaces under low-shear conditions also led to formation of subvisible particles but not low-MW aggregates (small oligomers) or visible species (8). In terms of particle count, it is difficult to compare quantitatively the extent of protein degradation between both studies: In the original work, MAb 1 was diluted significantly with buffer during desorption.

In our experimental design, antibody solutions placed on the rheometer but not subjected to shear showed several orders of magnitude less particle formation than did shear-stressed material. Our results suggest that particle formation is intensified by the amplified desorption effects of shear stress. The alternative histidine-based formulations of both MAb 1 and MAb 2 also demonstrated aggregation (subvisible particles) at lower shear and prolonged surface interaction, but they did not demonstrate an effect from the high shear of injection. Consistency in aggregate types formed in these systems suggests a common degradation pathway for the antibodies tested.

Although the aggregation mechanism appeared to be consistent for both MAb molecules, the extent of degradation varied substantially between them and their formulations. This demonstrates that probing surface adsorption and subsequent desorption due to shear stresses can be an effective methodology to use in forced-degradation studies (Figure 3).

Acknowledgments
Thanks to Aarti Gidh, James Colandene, and Trevor Wiley for valuable input and insightful discussions.

References
1 Fleischman ML, et al. ShippingInduced Aggregation in Therapeutic Antibodies: Utilization of a Scale-Down Model to Assess Degradation in Monoclonal Antibodies. J. Pharm. Sci. 12 December 2016, doi: 10.1016/j.xphs.2016.11.021.

2 Hawe A, et al. Forced Degradation of Therapeutic Proteins. J. Pharm. Sci. 101(3) 2012: 895–913.

3 Bee JS, et al. Response of a Concentrated Monoclonal Antibody Formulation to High Shear. Biotechnol. Bioeng. 103(5) 2009: 936–943.

4 Rathore N, et al. Characterization of Protein Rheology and Delivery Forces for Combination Products. J. Pharm. Sci. 101(12) 2012: 4472–4480.

5 Biddlecombe JG, et al. Determining Antibody Stability: Creation of Solid–Liquid Interfacial Effects within a High Shear Environment. Biotechnol. Prog. 23(5) 2007: 1218–1222.

6 Biddlecombe JG, et al. Factors Influencing Antibody Stability at Solid–Liquid Interfaces in a High Shear Environment. Biotechnol. Prog. 25(5) 2009: 1499–1507.

7 Wang W, et al. Antibody Structure, Instability, and Formulation. J. Pharm. Sci. 96(1) 2007: 1–26.

8 Perevozchikova T, et al. Protein Adsorption, Desorption and Aggregation Mediated By Solid–Liquid Interface. J. Pharm. Sci. 104(6) 2015: 1946–1959.

Corresponding author Tatiana Nanda is an investigator in biopharmaceutical product sciences; Jing He and Matthew Haas are senior scientists, and Igor Rusanov is a scientist in biopharmaceutical analytical sciences; Robert Sweder is an investigator, and Douglas Nesta is director of biopharmaceutical product sciences at
GlaxosmithKline, 709 Swedeland Road, King of Prussia, PA, 19406; 1-610-270-5885; tatiana.v.nanda@gsk.com. Shirley Shpungin is an associate scientist in drug product development at Janssen R&D, LLC (Malvern, PA), and Charlene Brisbane is director of drug product development and operations in biologics CMC at Teva Pharmaceuticals (West Chester, PA).

The post Aggregation from Shear Stress and Surface Interaction: Molecule-Specific or Universal Phenomenon? appeared first on BioProcess International.

Replication-defective (E1 gene-deleted) chimpanzee type-3 adenovirus (ChAd3) (HTTPS://MICROBEWIKI.KENYON.EDU/INDEX.PHP/ ADENOVIRUS-BASED_GENE_THERAPY:_A_PROMISING_ NOVEL_CANCER_THERAPY)

In December 2013, a two-year-old child in Guinea became the first person to be killed by Ebola in the most recent outbreak. In March of the following year, that outbreak was declared in West Africa. By mid-2014, the World Health Organization (WHO) had declared it to be a public health emergency of international concern and urged pharmaceutical companies to accelerate their development of candidate vaccines. At the peak of the outbreak in 2014, more than 1,200 new cases of Ebola were reported every week in Liberia, Sierra Leone, and Guinea. This was the most significant outbreak in Africa during the past 50 years.

In acknowledgment of WHO’s request, GSK initiated an Ebola project and gathered staff with relevant expertise to push vaccine candidates from laboratory scale to a large-scale product. The selected product candidate was developed by the Okairos Company (now Reithera), a small biotechnology company from which GSK acquired its technical platform just one year before the Ebola outbreak. Okairos had developed an innovative technology based on adenoviruses isolated from different species of primates, primarily chimpanzees. Previously, the company had collaborated with the US National Institutes of Health (NIH) to develop its adenoviral vector: a replication-defective (E1 gene-deleted) chimpanzee type-3 adenovirus (ChAd3, pictured) carrying a transgene encoding the glycoprotein of Zaïre Ebolavirus (1).

Using chimpanzee adenoviruses brings two main benefits. First, adenoviruses naturally elicit a CD8+ T-cell response, which is essential to fighting intracellular pathogens. Second, humans have low preexisting immunity against chimpanzee vectors — high preexisting immunity being a major drawback with using human-specific viruses.

To produce these adenoviruses, GSK uses a complementing cell line (HEK 293) expressing human Adenovirus 5 E1 sequence, allowing the growth of replication-defective, E1-deleted ChAd3. When used as a vaccination, the resulting viruses can infect and use cell machinery to express the transgene (in this case the favored Ebola transgene), but they cannot replicate without their own E1 sequence (2). In fact, the transgene is located where the E1 sequence was deleted.

The adenovirus vector technology introduces a new way to vaccinate people. Indeed, the concept of vaccination has evolved has evolved over a century from an empirical approach using natural pathogens through recombinant technology to produce pure and well-defined antigens. Unfortunately, the latter have not proven to elicit high levels of immunity. Conjugation and adjuvants were developed as a response in the 1990s. And in 2010, a reverse-vaccination approach (sequencing the whole pathogen genome and isolating key antigens) was at the origin of a meningococcal vaccine (3). The next-generation products include glycoconjugates, self-amplifying messenger ribonucleic acid (mRNA), new adjuvants, and adenoviruses.

A Scale-Up Challenge
GSK faced multiple and concomitant challenges for transforming a laboratory-scale vaccine candidate into a commercial-scale product in just a few months (instead of years). GMP cell banks and virus seeds were needed for ChAd3-EBO-Z vaccine manufacturing, and production yield had to be increased by 1 log, requiring improvements in cell culture and purification methods. We needed a formulation that would not require drug-product (DP) storage in a deep freeze. Finally, GSK would have to develop and qualify in-process analytical characterization methods for quality control (QC), with improved robustness that could support the release of cell banks, virus seeds, drug substance (DS), and DP in accordance with regulatory agency guidelines.

The primary challenges for the DP team were to stabilize ChAd3-EBO-Z at 2–8 °C and to rapidly develop process analytical tools and knowledge. Frozen storage is very difficult for shipping products to and throughout Africa. A buffer previously developed by Merck that could stabilize human adenovirus in a liquid state for several years was inappropriate for chimpanzee adenoviruses. However, because of its ethanol and high salt content, it is not the best choice for freezing or trying to develop a freeze-dried formulation.

Table 1: Main process analytical technologies (PATs) used during drug product development; GFP = green fluorescent protein

Analytical Development
Before working on thermostabilization of the vector, we focused on quality and analysis. We identified three critical quality attributes (CQAs) for this vaccine product: physicochemical integrity of the virus, functional infectivity (live virus), and particle content (viral quantity). Table 1 summarizes the tools we developed to give us results within a one-week timeframe for monitoring those parameters and to allow sustainable iterations throughout the DP development.

For physicochemical integrity, we monitored the size and degradation of virus particles using dynamic light scattering (DLS). We measured capsule integrity with a PicoGreen assay (Thermo Fisher Scientific), which is based on fluorescent staining of double-stranded DNA. When virus capsules are disrupted, they release DNA that fluoresces.

To monitor functional infectivity, we use two techniques. Cell culture infectious dose (CCID50) testing would not work for large numbers of samples, so we developed a higher throughput test based on fluorescence-activated cell sorting (FACS) that provides results in one day.

Finally, for particle counting, we used quantitative polymerase chain reaction (qPCR) to measure the number of copies of a cytomegalovirus promoter inserted into the viral genome. Later, we shifted to analytical ion-exchange chromatography for this parameter.

Formulation Development
Our purpose was to develop a lyophilized product, so we first looked at the glass-transition temperature of the formulation. With the original buffer, T_g′ was –51 °C, which is completely incompatible with lyophilization. To make it more suitable, we removed ethanol and ethylenediaminetetraacetic acid (EDTA) from the buffer ingredients and decreased the salt concentration while increasing sucrose to keep the formulation isotonic. The modifications improved T_g′ to –35 °C, which was now compatible with lyophilization cycles.

Figure 1: Impact of annealing on the freeze-drying cycle. Mathot F, Bourles E. Pharmaceutical Composition Comprising an Adenoviral Vector. WO 2017013169 A1 26 January 2017.

With the new “light” buffer, we investigated two lyophilization process variants, with and without an annealing step. That is when, during the freezing phase of freeze-drying, a product is maintained at a specified subfreezing temperature (slightly above T_g′) for a predetermined time period. That can cause Ostwald ripening of ice crystals and cryoconcentration of an amorphous matrix. Figure 1 shows the lyophilization cycle we applied, with annealing at the end of the freezing step, followed by primary and secondary drying.

Table 2: Impact of annealing step on the freeze-drying cycle; negative control is the starting adenoviral stock; positive control is the same material intentionally degraded for 30 minutes at 60 °C.

Table 2 shows the results. For every condition, we tested two controls: a freshly thawed DS diluted at the DP concentration to get a negative control and the same submitted to full degradation of 30 minutes at 60 °C for a forced-degradation positive control. We were surprised to find that the best conditions came with inclusion of the annealing process. PicoGreen assay results fell in the midrange (the lower, the better), and infectivity was ~30% (the higher, the better). Without annealing, the latter was only 2%. We hypothesize that this virus is very sensitive to cold denaturation (which makes sense for a virus that evolved to infect chimpanzees in Africa), and the annealing step decreases the interfacial surfaces that come into contact with it.

Figure 2: Replacing sucrose with trehalose and adding PS 80 as a surfactant provided the best stabilization. Composition A: ChAd3-EBO-Z 1.1011 VP/mL; Tris 10 mM, pH 7.5; histidine 10 mM; NaCl 25 mM; sucrose 8%; MgCl2 1 mM; polysorbate 80, 0.02% Composition B: ChAd3-EBO-Z 1.1011 VP/mL; Tris 10 mM, pH 7.5; histidine 10 mM; NaCl 6 mM (residual); trehalose 7%; sucrose 2% (residual); MgCl2 1 mM; polysorbate 80, 0.02% Composition C: ChAd3-EBO-Z 1.1011 VP/mL; Tris 10 mM, pH 7.5; histidine 10 mM; NaCl 6 mM (residual); trehalose 7%; sucrose 2% (residual); MgCl2 1 mM; poloxamer 188, 0.15%

Next, we tried to improve the lyophilization matrix by revisiting the lyoprotectant and surfactant composition in the buffer. We replaced the sucrose with trehalose and polysorbate 80 with poloxamer 188. When looking at the virus size by DLS as a CQA (Figure 2), we found the best stability came when sucrose was replaced by trehalose, with polysorbate 80 as a surfactant. Indeed, massive aggregation was seen when we used the poloxamer 188 during the lyo step. So we decided to keep the polysorbate 80 as a surfactant and further investigate whether trehalose was the best choice with it.

Figure 3: Matrix optimization — impact of reconstitution medium’s salinity (different rehydration media tested to reconstitute freeze-dried vaccines incubated for two months at +4 °C).  Preserved capsid integrity when NaCl-free media are used with the trehalosebased lyophilization matrix. Hypertonicity has no effect as long as NaCl-free media are used.

For that purpose, we produced two variants of the light buffer: one with sucrose, the other with trehalose. And we incubated drug products with each for two months at 4 °C, then hydrated them with different media. We tried water for injection (WFI), isotonic sucrose, isotonic trehalose, and two concentrations of NaCl (30 mM and 150 mM). Figure 3 shows the PicoGreen assay results that followed. (Again, the lower numbers are better here, showing no capsule disruption.) The best results came when we used a salt-free medium and with the trehalose base matrix. Based on those results, hypertonicity has no impact as long as a salt-free medium is used. We do not have an explanation for that yet, but we do know that sugar has unique glass-transition properties (much greater than other regularly used disaccharides).

Table 3: Fine-tuning the lyophilization matrix — investigating trehalose concentration and the effect of histidine

Finally, we tried to further optimize the lyophilization matrix by investigating higher concentrations of trehalose with and without histidine, which is a known stabilizer for adenoviruses (4). We found that the optimal conditions — determined by PicoGreen and infectivity assays — included both trehalose and histidine (Table 3). Infectivity was preserved at ~50% (a 0.3-log loss), meaning that half the product was completely destroyed after freeze-drying. In the field of live-virus vaccines, a 0.3-log loss is acceptable. But because we were responding to a health emergency, we could not sacrifice half of our production: Every dose produced was needed.

A Work in Progress
We have successfully improved the development of this lyophilized ChAd3-EBO-Z vaccine for the African market. From a 1-log product loss at the start, we achieved 0.3 log loss. We found it interesting that this adenovirus is very sensitive to the presence of NaCl and also that, by including trehalose in conjunction with polysorbate 80 surfactant, we could drastically improve product stability. An annealing step during lyophilization brings superior performance to virus stability and infectivity for this vaccine.

However, this version of the vaccine is not fully acceptable for a health emergency because of the induced process loss. We found a compromise for production of Ebola health emergency lots using a back-up solution (investigated in parallel) that requires keeping the vaccine at ≤–60 °C but allows for temperature excursions up to –20 °C for up to 10 months (thanks to the light buffer formulation) with no loss in infectivity. That makes it easier to ship to and around Africa for storage in dispensaries.

The story is not over for our lyophilized formulation of adenoviral vectors, however. We continue to develop freeze-dried vaccines as well as liquid dosage forms.

Acknowledgments
We acknowledge the work of GSK’s lyophilization and formulation teams in Belgium: Delphine Guillaume, Catherine Van Loo, Marie-Hélène Delannoy, Stéphane Godart, Vincent Ronsse, Olivier Despas, Alain Philippart. In collaboration with the Vaccine Research Center (VRC) for the development of the Ebola candidate, this work was sponsored and financially supported by GlaxoSmithKline Biologicals SA, the US Department of Health and Human Services’ Biomedical Advanced Research and Development Authority (BARDA), and the Bill and Melinda Gates Foundation.

References
1 Stanley DA, et al. Chimpanzee Adenovirus Vaccine Generates Acute and Durable Protective Immunity Against Ebolavirus Challenge. Nat. Med. 20(10) 2014: 1126–1129; doi:10.1038/nm.3702.

2 Cottingham MG, et al. Preventing Spontaneous Genetic Rearrangements in the Transgene Cassettes of Adenovirus Vectors. Biotechnol. Bioeng. 109(3) 2012: 719–728; doi:10.1002/bit.24342.

3 Rappuoli R, De Gregorio E. Vaccines: Novel Technologies for Vaccine Development. Curr. Opin. Immunol. August 2016; v–vii; doi:10.1016/j.coi.2016.07.001.

4 Evans RK, et al. Development of Stable Liquid Formulations for Adenovirus-Based Vaccines. J. Pharm. Sci. 93(10) 2004: 2458–2475; doi:10.1002/jps.20157.

Frédéric Mathot, PhD, is senior scientist; and Erwan Bourlès, PhD, is an expert scientist in drug-product technical research and development at GSK in Rixensart, Belgium. The authors are listed as inventors on an international patent application in the name of GlaxoSmithKline Biologicals SA: WO2017013169 A1, “Pharmaceutical Composition Comprising an Adenoviral Vector,” 20 July 2016. GlaxoSmithKline Biologicals SA was involved in all stages study conduct and analysis.

The post Development of a Freeze-Dried Ebola-Expressing Adenoviral Vector: Unexpected Findings and Problems Solved appeared first on BioProcess International.

Figure 1: Reactions in the SHP631 I2S activity assay: In the first step catalyzed by I2S, the substrate (IdoA2S-4MU) is hydrolyzed to IdoA-4MU and sulfate. In the second step, 4MU is released by the action of IDUA. Fluorescence quantitation of 4MU product is carried out following a high-pH quench.

The recombinant fusion protein SHP631 consists of a chimeric monoclonal antibody binding to human insulin receptor and iduronate-2-sulfatase (I2S). This product is being developed as an enzyme replacement therapy to treat cognitive symptoms of Hunter’s syndrome. Because the current therapy (idursulfase, brand name Elaprase from Shire) cannot cross the blood–brain barrier (BBB), SHP631 is being developed to do so, enabling the presence of I2S in the brain. The enzymatic activity of this molecule is measured using the substrate 4-methyl umbelliferyl-α-L-idopyranosiduronic acid 2-sulfate (IdoA2S-4MU). The measured specific activity of SHP631 in-process samples tends to be lower when measured at lower dilution factor (DF) in the assay, indicating an apparent matrix effect. So assay results are difficult to compare among different SHP631 in-process samples. Here, we identify possible sources of inhibition and describe a method to correct activity values for the observed inhibition.

Materials and Methods
Carbosynth custom synthesied IdoA2S4MU, and Shire generated α-L-iduronidase (IDUA). We obtained 4-methyl-umbelliferone (4-MU) sodium salt from Sigma-Aldrich and Opti CHO culture medium from Thermo Fisher Scientific. We measured I2S activity in SHP631 using a two-step plate-based method with fluorescence detection as described by Voznyi et al. (1) with slight modification. We initiated the first reaction catalyzed by SHP631 by mixing equal volumes of substrate solution and the diluted in-process sample; the substrate IdoA2S-4MU is hydrolyzed to 4-methyl-umbelliferyl-α-Lidopyranosiduronic acid (IdoA-4MU) and sulfate. In the second reaction, we achieved a complete conversion of IdoA-4MU to 4MU by adding excess amount of IDUA (Figure 1).

Routine Assay of SHP631 I2S Activity: The reaction is carried out in 96-well polymerase chain reaction (PCR) plates with a temperature controlled thermocycler. We initiated the reaction by mixing 20 μL each of 2 mM IdoA2S-4MU substrate solution and 5 ng/mL SHP631 sample solution in 2× assay buffer. That generated a 50 mM acetate-buffered reaction mixture, pH 5.2, containing 0.03 mg/mL of bovine serum albumin (BSA), which was incubated for one hour at 37 °C. We added 40 µL of 25 µg/mL IDUA in McIlvaine’s buffer (0.40 M sodium phosphate, 0.20 M citrate, 0.02 % sodium azide, pH 4.5) to arrest the I2S reaction.

The second-step reaction with IDUA was incubated for an additional hour at the same temperature. We quenched the second-step reaction by adding 200 µL of 0.5 M sodium carbonate solution, pH 10.7. Finally, we measured the observed fluorescence of 4-MU at λ_ex and λ_em of 365 and 450 nm, respectively.

We modified the above assay as necessary to understand matrix interference. The “Buffers” box above lists buffers tested for the matrix effect in the study.

Equations

Determination of K_m: We carried out the reaction in the same way as the routine assay with SHP631 sample, except the final substrate IdoA2S-4MU concentration was varied. The substrate solution was serially diluted before mixing with SHP631 to give concentrations of 31.25–2,000 µM in the final reaction mixture. We determined the Michaelis-Menten constant K_m by fitting the dependence of the observed activity on substrate concentration [S] to the Michaelis-Menten equation (Equation 1).

Assay in the Presence of Buffer Matrix: To assess the buffer matrix effect, we mixed a fixed amount of SHP631 with different diluted in-process buffers. Exactly 10 µL each diluted buffer solution and substrate solution were mixed with 20 µL of enzyme solution in 2× assay buffer to start the first-step reaction. The assay proceeded as described in the “Routine Assay” section above. The final enzyme concentration in the reaction was 2.5 ng/mL.

Plots of enzyme specific activity (v/ [E]) — where v is rate of reaction and [E] is enzyme concentration — and inverse of dilution factor (1/DF) were fitted to the equation for rapid equilibrium mixed inhibition as per Equation 2 (2) using a value of K_m = 386 µM determined from Michaelis–Menten analysis of SHP631 as described above. In Equation 2, [C/DF] is the apparent concentration of inhibitory components in the reactive mixture, K_is is the inhibition constant for inhibitor binding to the free enzyme, and K_ii is the inhibition constant for inhibitor binding to the ES complex.

When the substrate concentration is fixed, parameters K_ii and K_is become redundant. So the same fit curve can be obtained regardless of whether the inhibition is competitive, noncompetitive, uncompetitive, or mixed. Once we determined the necessary parameters from the fit, the same inhibition-free activity (v₀/[E]) can be calculated using Equation 3, regardless of which inhibition modes are operating. In that equation, v₀ is the catalytic rate in the absence of inhibitory component.

Figure 2: I2S activity of 2.5 ng/mL SHP 631 at different buffer concentrations

Simplification of the Data Treatment By Linearization of the Rate Equation 2: Inhibition-free activity also can be calculated using the linearized Equation 4, which can be simplified to Equation 5. Thus, activity in absence of inhibitors can be calculated after fitting data to the linear Equation 6. Note, however, that in simple linear fitting of the reciprocal plot, smaller values of v/[E] with larger random errors are heavily weighted and could distort results. Therefore care must be taken to prevent this by applying an appropriate weighting factor to data points or by simply omitting extremely low values from the fit.

Figure 3: Least-squares fit to the linearized Equation 4

Results and Discussion
The I2S specific activity assay of SHP631 in-process samples are carried out routinely by diluting the samples to a target concentration of 2.5 ng/mL for activity measurement. However, we noticed that the measured specific activity for different in-process samples can depend on the concentration of the diluted sample used in the assay, with higher concentrations (lower dilution factors) of sample in the assay giving lower specific activity values. We performed further experiments to determine whether that effect is caused by inhibition by in-process sample buffer matrix as described below.

Figure 4: Examination of the dependence of observed specific activity on dilution of buffer matrix at constant enzyme concentration (green) or dilution of actual in-process sample (both E and buffer matrix are being diluted) (blue).

To assess the effect of buffer matrix on measured specific activity values, we carried out the assay with a fixed concentration of SHP631 in different concentrations of six in-process buffer solutions (Table 1a and 1b). Specific activity decreased at higher buffer concentration with buffers 1–4. Specific activity had a smaller decrease with buffers 5 and 6. Figure 2 shows the results.

Values of matrix-free activity (v₀/[E]) and parameters (K_is/C, with K_ii = ∞) can be determined from the fit of the plot of observed specific activity (v/[E]) and DF to Equation 4 for each in-process sample buffer (Table 1a). Once parameters from the nonlinear fit are known, matrix-free activity v₀/[E] can be determined using data from a single concentration point using the same equation. Values of inhibition-free activity determined from single concentration points were consistent, regardless of dilution factor. The exception is the case of high inhibition (low specific activity), where relative random errors are larger. Matrix-free activity also can be determined by fitting data to the linearized Equation 5 if low activity values are omitted because of high random error (Figure 3). Calculated matrix-free activity values were comparable with those from nonlinear fit (Table 1a).

Experimental data for buffers 3, 4, and 6 were overlaid with results of corresponding real in-process sample serially diluted in water across the dilution range indicated in Figure 4. Results show that for buffers 3 and 6, inhibition is much stronger for real in-process samples than for buffer alone (constant [E]). Results indicate that observed inhibition in real samples in buffers 3 and 6 is not attributable to buffer-matrix alone.

Table 1a: Calculated matrix-free activity (v₀/[E]) of SH631 in different buffers by nonlinear fit of enzyme-specific activity measured at constant enzyme concentration (2.5 ng/mL) and varying in-process buffer concentration (C/DF); the values in blue-shaded boxes represent activity calculated from curve-fitting of all data points. Numbers in red represent %inhibition values >80%.

Table 1b: Calculated matrix-free activity (v₀/[E]) of SH631 in different buffers by linear fit of enzyme-specific activity measured at constant enzyme concentration (2.5 ng/mL) and varying in-process buffer concentration (C/DF); the values in the blue-shaded box represent the activity calculated from the curve-fitting of all the data points. The numbers in red represent %inhibition values >80%.

• Substrate depletion — more substrate is consumed at smaller DF (higher enzyme concentration), thereby reducing v/[E].
• Product inhibition — at lower DF of in-process samples, enzyme concentration is higher, so more product is generated, which causes greater inhibition.

Figure 5: Representation of experiment with buffer 3 to separate the effects of matrix and enzyme concentration on measured specific activity; Set A = regular assay with serially diluted sample; Set B = sample set to assess the product inhibition (and possible substrate depeletion) effect; Set C = sample set to assess the matrix effect of in-process buffer.

To investigate further the source of inhibition effects, we created three sets of samples with Buffer 3 (SHP631 drug substance formulation buffer). Set A had varied enzyme and buffer concentrations. Set B had varied enzyme concentrations and a constant buffer concentration. Set C had a constant enzyme concentration and varied buffer concentrations (Figure 5).

Substrate depletion is unlikely to have caused the observed inhibition because the amount of substrate converted to product under conditions used in these studies was <6%, so substrate concentration [S] ≈ [S]_initial (Figure 6).

Figure 6: Substrate depletion in the experiment to separate the inhibition effects shown in Figure 5; depletion of the substrate was calculated from the detected concentration of 4MU and the initial concentration of the substrate.

Data from sample sets A (varied [E], varied [buffer]) and B (varied [E], constant [buffer]) indicate a stronger dependence of specific activity on concentration (proportional to 1/DF) than in set C (constant [E], varied [buffer]). Results in Figure 7 show that for samples in SHP631 drug substance formulation, buffer-matrix inhibition is not caused by matrix alone but probably can be attributed to product inhibition.

Our results indicate that both matrix and product can cause inhibition (decreased specific activity of SHP631 at lower sample dilution factor). For SHP631, we demonstrated that in some in-process sample types, product inhibition is the dominant source of inhibition observed. The relative importance of matrix and product inhibition is determined by the nature of the matrix and dilution factor used in the assay.

Figure 7: Results of experiments to measure SHP631 specific activity in DS formulation buffer matrix under three sets of conditions: (A) regular serial dilution, with varied [E] and varied [buffer]; (B) product inhibition, with varied [E] and constant [buffer]; and (C) matrix effect, with constant [E] and varied [buffer]

Based on our study, we propose the following method to determine inhibition-free specific activity of in-process samples when enzyme concentration is sufficiently high and matrix effects are insignificant (e.g., when product inhibition is expected to be the dominant source of inhibition):

• Determine dependence of specific activity on [E] using purified SHP631. Perform a linear fit of a plot of [E]/v and 1/DF to obtain slope (a from Equation 4).
• Measure specific activity (v/[E]) of in-process sample at a single defined dilution factor.
• Calculate inhibition-free specific activity (v₀/[E]) using Equation 6 and
  the slope a from the first step.

For in-process sample types in which the source of inhibition has not been identified, inhibition-free specific activity can be determined by assaying at different dilutions, plotting [E]/v and 1/DF and extrapolating to infinite dilution (1/DF = 0). That would allow for direct comparison of enzyme activity values across different in-process sample types.

Acknowledgments
This research was funded by Shire. All authors are employees of Shire and hold stock and/or stock options in Shire.

References
1 Voznyi YV, Keulemans JLM, van Diggelen OP. A Fluorimetric Enzyme Assay for the Diagnosis of MPS II (Hunter Disease). J. Inherit. Metab. Dis. 24(6) 2001: 675–680.

2 Segel IH. Enzyme Kinetics. John Wiley & Sons: New York, NY, 1975.

Corresponding author Takayuki Nakano is senior development specialist, Brenda Kellogg is senior scientist, and Kannappan VeeraRagavan is head of method development at Shire Pharmaceuticals, 300 Shire Way, Lexington, MA 02421; tnakano-c@shire.com.

The post Apparent Matrix Effects in an Iduronate 2-Sulfatase Specific Activity Assay appeared first on BioProcess International.

Figure 1: Comparing considerations for analytical development of coformulated products with those of coadministered products

Coformulation of two or more proteins in a single formulation is an emerging approach to delivering multiple biotherapeutics that previously have been administered in sequence. This approach brings multiple benefits to all stakeholders. Foremost for patients, the primary benefits are combined therapeutic effects and improved convenience (e.g., fewer administration events). Healthcare providers see logistical benefits and decreased risk of medical errors. Additionally, coformulations also simplify manufacturing logistics, reduce costs of packaging and distribution, and provide new opportunities for product portfolio development and marketing. However, a coformulation approach can be used only for therapeutics that share the same dosing frequency, and it reduces dosing flexibility because of the constant ratio of products in formulation.

Drug-product manufacturers have a few options for delivering combination therapies to provide the complementary therapeutic effects of two or more biologics at once: sequential administration, coadministration by combining multiple single therapies into a single bag for intravenous (IV) infusion, or fixed-dose combination. The latter strategy commonly is used for small-molecule drugs and simple proteins.

The most prominent examples can be found in the diabetes therapeutic area, with more than 20 coformulated products currently available (1). Safety, efficacy, pharmacokinetics, and pharmacodynamics have been studied extensively and proven for two forms of coformulated insulin: aspart and diglutec (2). Two different proteins also may be coformulated, such as insulin and the enzyme hyaluronidase (3) or hyaluronidase with an antibody to facilitate subcutaneous application (4). In the immunooncology therapeutic space, a growing trend involves coformulating multiple monoclonal antibodies (MAbs) that target multiple receptors or targets: e.g., anti-PD-1 antibody in combination with another MAb, such as anti-CTLA-4 or anti-LAG-3 (5). A coformulation approach was reported recently for two broadly neutralizing antibodies in treatment of human immunodeficiency virus (HIV) (6). Other combinations of various biologics or modalities surely will be developed successfully in the future.

Formulation Development
Compared with single-agent monotherapy products, a few additional decisions must be made and considerations included in formulation development for two or more coformulated molecules. Concerns include optimal selection of dosing, the ratio of active molecules in formulation, and administration frequency for a given indication. Coformulated products currently are fixed-dose combinations that are considered for treatment of certain indications by acting on multiple different molecular targets and producing a synergetic therapeutic effect. Formulators must consider carefully the composition or ratio of active products to guarantee their stability and minimize inherent liabilities of different protein molecules. Furthermore, formulation development attributes such as pH, buffer, and excipient composition need to be adequately selected to preserve or even increase the stability of these drug products and to reduce potential, unfavorable heterogeneous interactions.

Analytical Development Coformulated products also increase the complexity of analytical development and product characterization. Analytical methods originally developed for individual products may need to be further developed for coformulations. Figure 1 summarizes several constraints and considerations for analytical development specific to transition from coadministered to coformulated products.

Protein therapeutics for coadministration usually are combined in an IV bag. Exposure to ambient temperature and visible light are short-term, so those mild conditions typically do not induce product degradation. On the other hand, individual proteins typically undergo some degradation processes during long-term storage — effects that, to an extent, can be expected with coformulations. The stress conditions present during coformulation manufacturing, therefore, could affect what becomes a critical product liability later on; e.g., aggregation, deamidation, oxidation, and so on. In addition, coformulation conditions could lead to formation of heterogeneous (mixed) aggregates.

Figure 2: Interactions between active pharmaceutical ingredients (APIs) in coformulated and coadministered products

Interactions in Coformulation
In addition to refining analytical methods for coformulated products, some other concerns are unique to coformulation and coadministration. The formation of heterogeneous aggregates needs to be considered in coformulation development. In general, three types of interaction can be expected in complex mixtures of active pharmaceutical ingredients (APIs), and they can occur in either coadministration or coformulation (Figure 2): antibody–excipient interactions and antibody interactions with the same or other antibodies.

Antibody–Excipient Interactions: Some excipients are added to stabilize protein or control viscosity, and their roles and mechanisms of action generally are understood to some extent.

Antibody–Antibody Interactions (Homogeneous): Some form of interaction is expected in formulations and manifests as viscosity. Generally, such interactions can be controlled with excipients. Interactions leading to aggregation or particulation are the main concern.

Interactions Between Antibodies 1 and 2 (Heterogeneous): Similar to homogeneous protein interactions, some form of interprotein interaction is expected and also manifests as increased viscosity. The concern for coformulated products is formation of heterogeneous species: dimers, larger aggregates, and possibly subvisible/visible particles. The role these aggregated species play in safety and immunogenicity is not well understood.

Although in vitro interactions are possible during measurement and analysis, in vivo interactions pose a more serious challenge for pharmacodynamic and pharmacokinetic evaluations. Here, we focus primarily on analytical strategies applicable to coformulations — and possibly to coadministration as well.

Figure 3: Size-exclusion chromatography (SEC) and its application to coformulated products

Analytical Strategies
Among available analytical techniques, size-exclusion chromatography (SEC) is the most widely used (and accepted) method for evaluating aggregation content of drug products. As Figure 3 illustrates, SEC reports aggregation in coformulated product as “total aggregation” or “total purity” (monomer).

In some possible scenarios, total aggregation values could relate to individual products; in others, they could indicate heterogeneous interactions between antibodies. Such behavior was observed with coformulation of anti-PD1-L1 and anti-CTLA-4 antibodies (7). Those interactions may result in more complex behavior in formulation, as has been shown for bispecifics compared with coformulation and monotherapies (8).

Table 1: The potential of currently used methods in product characterization for use in coformulation analytics

However, because of SEC’s lack of specificity, probing aggregation with this technique is significantly challenging. It is difficult to distinguish which molecular species are involved in aggregate formation. Thus, for analyzing coformulations, alternative separation techniques that offer the potential to trace the source of product degradation must be considered. For this purpose, newer concepts such as two-dimensional (2D) or mixed-mode chromatography combining different separation principles (e.g., size exclusion with hydrophobic interaction) might be more suitable than SEC. 2D chromatography is useful in characterizing single-entity products (9) and undoubtedly has potential to be applied for coformulated products as well. Other possible alternatives to simple SEC include spectroscopy and separation techniques that do not rely on size.

In summary, multiple methods are used routinely for product characterization, several of which are “interaction sensitive” to an extent — or can at least detect some consequences of interactions (Table 1).

Figure 4: Existing and emerging interaction-sensitive techniques to detect mixed aggregates in coformulated products; ELISA = enzyme-linked immunosorbent assay, FL = fluorescence labeling, FRET = Förster resonance energy transfer, KinExA = kinetic exclusion assay, NMR = nuclear magnetic resonance, SANS = small-angle neutron scattering, SAXS = small-angle X-ray scattering

Emerging Analytical Methods: Currently used analytical methods originally were not developed for coformulations or for evaluating interactions between antibodies (or any other proteins, for that matter). They are either not sensitive enough or unable to detect interactions over the timescale of a product’s shelf life. Thus, the industry suffers an outstanding need for developing new methods and strategies that can be applied to detecting mixed aggregates through coformulated products’ shelf lives. Figure 4 highlights a number of promising strategies to detect mixed aggregates.

It is important to consider method capabilities when selecting analytical technologies. A general, widely accepted approach to characterization of interactions is measuring k_D, the parameter of bulk viscosity/restricted diffusion interaction; however, it probably would be difficult to apply that for the kinetics of heterogeneous aggregation. Individual products are known to aggregate over long storage periods, and possible aggregation (including heterogeneous) is to be expected in coformulations stored over long periods. Currently, no standard methods have been established or accepted by the industry for measuring mixed aggregates. However, a few emerging techniques show great potential to detect and/or quantify heterogeneous aggregates based on mass spectrometry (MS), fluorescence labeling (FL), and nuclear magnetic resonance analysis (NMR).

Mass Spectrometry: Complex mixture analysis remains, in some regard, one of the greatest challenges in the field of measurement science. Especially when coupled to online separations such as reversed-phase liquid chromatography or capillary electrophoresis (CE), high-resolution MS offers one of the most powerful tools for addressing these challenges. Advanced MS analysis of large proteins is pertinent to developing methods for characterization of complex mixtures of proteins (e.g., MAbs) because it can detect weakly bound dimers. Heterogeneous dimers could be detected in coformulations using combined separation techniques (e.g., CE-MS); gas-phase fragmentation chemistries, such as electron-transfer dissociation (ETD), which is used for top-down protein analysis; and deconvolution algorithms of complex mass spectra.

Fluorescence Labeling: Development of MAb-based coformulations raises new challenges for high-throughput analyses. First, it is difficult to understand whether MAbs in mono- and coformulations present the same chemical stability profiles. Second is the problem of understanding potential protein–protein and protein–excipient interactions.

Those challenges require screening a vast array of conditions: e.g., protein concentrations, stress conditions, and both the types of excipients and combinations thereof. That’s why biochemical fluorescent tools compatible with a high-throughput approach are used to monitor both protein oxidation reactions and the molecular dynamics of protein–protein and protein–excipient interactions. Förster-resonance energy transfer (FRET) is a common technique for detecting protein interactions. It requires labeling antibodies with fluorescent dyes and therefore comes with the risk of modifying antibody properties. Indeed, the most common labeling chemistry is based on derivatization of primary amines, which can change the charge densities of labeled proteins. Thus, despite the high sensitivity of fluorescent assays, implementation of such techniques in drug formulation sciences requires acute control of labeling chemistries.

Nuclear Magnetic Resonance: Translational self-diffusion and relaxation measurements are sensitive to interactions over a broad range of affinities (mM–pM), specificities, and stoichiometries. Proton NMR spectra provide quantitative concentration measurements; 1D and 2D NMR profiling methods can differentiate proteins in mixtures based on chemical shift patterns that are unique to each protein. Formulated protein mixtures can be measured thusly in situ with no labeling required.

Computational Tools: Additionally, computational modeling offers complementary tools to characterize and predict the behavior of drug substances in coformulations. A number of protein models — ranging from atomistic to simplified coarse-grained and colloidal representations — can be used to probe different molecular processes that alter the stability of individual products and how they will be influenced by crossinteractions among proteins and/or excipients in formulation.

Some drug-product attributes in coformulations might be estimated readily through molecular simulations and theoretical modeling, including virial coefficients, diffusivity, viscosity, solubility, critical opalescence, and phase behavior (e.g., criticality for liquid–liquid equilibrium and crystallization). That could help guide development of coformulations and greatly facilitate identification of a suitable drug formulations (including optimal ratios of active products).

Furthermore, computational models for biotherapeutic coformulations could prove to be invaluable for analyzing high-resolution, structural data such as those obtained from NMR, MS, and small-angle scattering experiments. These models could help formulators distinguish the contributions from each individual species to experimental data and help relate macroscopic behavior to specific molecular events.

Work To Be Done
Coformulation of two or more antibodies brings multiple benefits and concomitant challenges. These are issues that the biopharmaceutical industry is addressing. Further development of existing methods originally used for singly formulated products and methods for detection of heterogeneous interactions are the most urgent needs to promote successful development of coformulated products. Present challenges include evaluating possible immunogenicity of heterogeneous high–molecular-weight species and establishing clinical and regulatory strategies for coformulated products.

References
1 Trujillo JM. Role of Combination Therapy or Coformulation Products in Treatment of Type 2 Diabetes. Pharmacy Today 24(8) 2018: 50–64.

2 Kumar A, et al. Clinical Use of the Coformulation of Insulin Degludec and Insulin Aspart. Int. J. Clin. Pract. 70(8) 2016: 657–667.

3 Yang T-H, et al. Stable Formulations of a Hyaluronan- Degrading Enzyme Related Applications. US Patent 9993529 B2, 12 June 2018.

4 Shpilberg O, Jackisch C. Subcutaneous Administration of Rituximab (MabThera) and Trastuzumab (Herceptin) Using Hyaluronidase. Br. J. Cancer 109(6) 2013: 1556–1561.

5 Sadineni V, Quan Y, Kaserer W. Compositions Comprising a Combination of an Anti-PD-1 Antibody and Another Antibody. US Patent 20160304607 (pending) 2016.

6 Patel A, et al. Coformulation of Broadly Neutralizing Antibodies 3BNC117 and PGT121: Analytical Challenges During Preformulation Characterization and Storage Stability Studies. J. Pharm. Sci. 107(12) 2018: 3032–3046.

7 Du J, Shah A. Compositions Comprising Coformulation of Anti-PD-L1 and Anti-CTLA-4 Antibodies. US Patent 2017/0306025 A1, 26 October 2017.

8 Woldeyes MA, et al. Viscosities and Protein Interactions of Bispecific Antibodies and Their Monospecific Mixtures. Mol. Pharm. 15(10) 2018: 4745−4755.

9 Beach A, Wassmann P, Lorenz T. Analytical Strategies for Developability Assessment of Therapeutic Proteins. Am. Pharm. Rev. July 26, 2018.

Corresponding author George Svitel (juraj.svitel@merck.com) is a principal scientist, Marco Blanco is a senior scientist, and Jason Cheung is executive director of pharmaceutical sciences; Olivier Mozziconacci is an associate principal scientist in preclinical development; and Mark McCoy is a principal scientist in chemistry at Merck & Co., Inc. in Kenilworth, NJ.

The post Analytical Strategies for Fixed-Dose Coformulated Protein Therapeutics appeared first on BioProcess International.

Calculations, including statistical tolerance intervals, can assist in the development and revision of specification acceptance criteria. Manufacturing results for attributes of a biopharmaceutical product can be positively autocorrelated. The sample standard deviation — calculated from limited, positively autocorrelated data — tends to underestimate the long-term process standard deviation (1). In this article, simulated data are used to assess the relative performance of statistical tolerance intervals, intervals calculated using the minimum process performance index Ppk approach, and the sample range. Prevalence…

The post Biopharmaceutical Product Specification Limits and Autocorrelated Data appeared first on BioProcess International.

Many physical and chemical factors can affect the quality, safety, and efficacy of biopharmaceutical products, particularly after long-term storage in a container–closure system that can be subject to variations in temperature and light, as well as agitation with shipping and handling. Proteins are inherently complex physiochemically, from their primary amino acid sequences to their higher-order structures, and they require specific conditions to maintain their integrity and functionality. Advanced biological therapies can be even more complicated and particular about their environments.…

The post Stability Testing: Monitoring Biological Product Quality Over Time appeared first on BioProcess International.