Developing Lyophilized Formulations for Protein Biopharmaceuticals Containing Salt that Produce Placebos of Corresponding Appearance.

Obtaining an elegant finished pharmaceutical product remains a problem when salt concentration is high, protein concentration is low, and particularly when both conditions are combined. We propose a simple approach to develop a robust lyophilized formulation in the presence of salt and at low protein concentration. We combine this with a commercially viable lyophilization cycle that can serve as a starting point for many protein-based pharmaceutical products. In this manner the formulator scientist, even with little experience, can develop a robust and visually acceptable lyophilized product. In addition, this platform allows the production of placebo vials with no visual differences compared to the active product.

Universal Qualification of Analytical Procedures for Characterization and Control of Biologics.

A diverse set of analytical tools is required to characterize the complex structural properties of biopharmaceutical products and to ensure their quality, stability, safety, and efficacy. It is generally necessary to demonstrate that such tools are capable of measuring one or more intended attribute(s) of the product with a desired degree of precision, accuracy, linearity, specificity and sensitivity. Here we present a general framework upon which experiments may be designed to establish analytical procedure performance, predicated on the hypothesis that many analytical procedures have universal performance characteristics - that is, the validity of the measured result is a function of the measurement system and data characteristics and is not a function of the specific analyte being measured. Using simulated data, we demonstrate that the generalized approach improves the scientific validity of resulting descriptions of procedure performance by reducing the incidence of false failures and missed faults during future use of the procedure. Broad adoption of these principles will facilitate an improved understanding of procedure performance characteristics while requiring fewer human resources for procedure qualification studies.

Determining Spectroscopic Quantitation Limits for Misfolded Structures.

Protein secondary structures are frequently assessed using infrared and circular dichroism spectroscopies during drug development (e.g., during product comparability and biosimilarity studies, reference standard characterization, etc.) However, there is little information on the lower limits of quantitation of structural misfolds and impurities for these methods. A model system using a monoclonal antibody reference material was spiked at various levels with a protein that had a significantly different secondary structure to represent the presence of a stable and discreet structural misfold. The ability of circular dichroism, transmission Fourier transform infrared spectroscopy and microfluidic modulation spectroscopy, along with various spectral comparison algorithms, were assessed for their ability to detect the presence and quantify the amount of the misfolded structure.

A multi-component classifier for nonalcoholic fatty liver disease (NAFLD) based on genomic, proteomic, and phenomic data domains.

Non-alcoholic fatty liver disease (NAFLD) represents a spectrum of conditions that include steatohepatitis and fibrosis that are thought to emanate from hepatic steatosis. Few robust biomarkers or diagnostic tests have been developed for hepatic steatosis in the setting of obesity. We have developed a multi-component classifier for hepatic steatosis comprised of phenotypic, genomic, and proteomic variables using data from 576 adults with extreme obesity who underwent bariatric surgery and intra-operative liver biopsy. Using a 443 patient training set, protein biomarker discovery was performed using the highly multiplexed SOMAscan® proteomic assay, a set of 19 clinical variables, and the steatosis predisposing PNPLA3 rs738409 single nucleotide polymorphism genotype status. The most stable markers were selected using a stability selection algorithm with a L1-regularized logistic regression kernel and were then fitted with logistic regression models to classify steatosis, that were then tested against a 133 sample blinded verification set. The highest area under the ROC curve (AUC) for steatosis of PNPLA3 rs738409 genotype, 8 proteins, or 19 phenotypic variables was 0.913, whereas the final classifier that included variables from all three domains had an AUC of 0.935. These data indicate that multi-domain modeling has better predictive power than comprehensive analysis of variables from a single domain.

Technical Decision Making With Higher Order Structure Data: Perspectives on Higher Order Structure Characterization From the Biopharmaceutical Industry.

Characterization of the higher order structure (HOS) of protein-based biopharmaceutical products is an important aspect of their development. Opinions vary about how best to apply biophysical methods, in which contexts to use these methods, and how to use the resulting data to make technical decisions as drug candidates are commercialized [Gabrielson JP, Weiss WF IV. J Pharm Sci. 2015;104(4):1240-1245]. The aim of this commentary is to provide guidance for the development and implementation of a robust and comprehensive HOS characterization strategy. We first consider important concepts involved in developing a strategy that is appropriately suited to a particular biologic, and then discuss ways industry can partner with academia, technology companies, government laboratories, and regulatory agencies to improve the consistency with which HOS characterization is applied across the biopharmaceutical industry.

Rapid Identification and Characterization of Formulated Protein Products by Raman Spectroscopy Coupled with Discriminant Analysis.

UNLABELLED: The rapid identification of protein drug products for packaging and receiving can significantly reduce disposition cycle time, and thereby improve the efficiency and productivity of the supply chain to better meet the needs of patients. In this feasibility study, we demonstrate a novel methodology that combines Raman spectroscopy with discriminant analysis that can be used for rapid identification or verification of finished products. With this methodology, Raman spectra of formulated therapeutic proteins were collected non-invasively with the samples either in a quartz cuvette or in the original glass vials, and analyzed without subtraction of buffer or placebo solutions. The algorithm used for the discriminant analysis was Mahalanobis distance by principal component analysis with residuals. In addition to product identification, the methodology has the potential to be used for characterizing formulated proteins when exposed to external stresses based on the changes of Mahalanobis distances. LAY ABSTRACT: The rapid identification of protein drug products for packaging and receiving can significantly reduce disposition cycle time, and thereby improve the efficiency and productivity of the supply chain. In this study, we demonstrate a novel methodology that combines Raman spectroscopy with discriminant analysis to rapidly identify formulated proteins non-invasively.

Guidance to Achieve Accurate Aggregate Quantitation in Biopharmaceuticals by SV-AUC.

The levels and types of aggregates present in protein biopharmaceuticals must be assessed during all stages of product development, manufacturing, and storage of the finished product. Routine monitoring of aggregate levels in biopharmaceuticals is typically achieved by size exclusion chromatography (SEC) due to its high precision, speed, robustness, and simplicity to operate. However, SEC is error prone and requires careful method development to ensure accuracy of reported aggregate levels. Sedimentation velocity analytical ultracentrifugation (SV-AUC) is an orthogonal technique that can be used to measure protein aggregation without many of the potential inaccuracies of SEC. In this chapter, we discuss applications of SV-AUC during biopharmaceutical development and how characteristics of the technique make it better suited for some applications than others. We then discuss the elements of a comprehensive analytical control strategy for SV-AUC. Successful implementation of these analytical control elements ensures that SV-AUC provides continued value over the long time frames necessary to bring biopharmaceuticals to market.

Technical decision-making with higher order structure data: starting a new dialogue.

Characterization of the higher order structure (HOS) of biological products has been growing in importance in recent years. Scientists in the biopharmaceutical industry, academic researchers, and regulators are all increasingly aware of the critical role that HOS plays in maintaining the stability and intended biological function of biopharmaceutical products. We organized a consortium of scientists and researchers from industry and academic institutions to address how HOS data can be used most effectively to drive decisions during product development. In this commentary, we introduce the purpose, objectives, and scope of the consortium and then provide some brief points to consider in the context of characterizing HOS of biopharmaceutical products. Scientific advances in HOS analysis, as well as continued dialogue among academia, industry, and regulatory agencies will ensure that appropriate methodologies are used to inform technical decision-making during biopharmaceutical development.

Technical decision making with higher order structure data: higher order structure characterization during protein therapeutic candidate screening.

Protein therapeutics differ considerably from small molecule drugs because of the presence of higher order structure (HOS), post-translational modifications, inherent molecular heterogeneity, and unique stability profiles. At early stages of development, multiple molecular candidates are often produced for the same biological target. In order to select the most promising molecule for further development, studies are carried out to compare and rank order the candidates in terms of their manufacturability, purity, and stability profiles. This note reports a case study on the use of selected HOS characterization methods for candidate selection and the role of HOS data in identifying potential challenges that may be avoided by selecting the optimal molecular entity for continued development.

Technical decision making with higher order structure data: utilization of differential scanning calorimetry to elucidate critical protein structural changes resulting from oxidation.

Differential scanning calorimetry (DSC) is a useful tool for monitoring thermal stability of the molecular conformation of proteins. Here, we present an example of the sensitivity of DSC to changes in stability arising from a common chemical degradation pathway, oxidation. This Note is part of a series of industry case studies demonstrating the application of higher order structure data for technical decision making. For this study, six protein products from three structural classes were evaluated at multiple levels of oxidation. For each protein, the melting temperature (Tm ) decreased linearly as a function of oxidation; however, differences in the rate of change in Tm , as well as differences in domain Tm stability were observed across and within structural classes. For one protein, analysis of the impact of oxidation on protein function was also performed. For this protein, DSC was shown to be a leading indicator of decreased antigen binding suggesting a subtle conformation change may be underway that can be detected using DSC prior to any observable impact on product potency. Detectable changes in oxidized methionine by mass spectrometry (MS) occurred at oxidation levels below those with a detectable conformational or functional impact. Therefore, by using MS, DSC, and relative potency methods in concert, the intricate relationship between a primary structural modification, changes in conformational stability, and functional impact can be elucidated.

Quantitative spectral comparison by weighted spectral difference for protein higher order structure confirmation.

Previously, different approaches of spectral comparison were evaluated, and the spectral difference (SD) method was shown to be valuable for its linearity with spectral changes and its independence on data spacing (Anal. Biochem. 434 (2013) 153-165). In this note, we present an enhancement of the SD calculation, referred to as the "weighted spectral difference" (WSD), by implementing a weighting function based on relative signal magnitude. While maintaining the advantages of the SD method, WSD improves the method sensitivity to spectral changes and tolerance for baseline inclusion. Furthermore, a generalized formula is presented to unify further development of approaches to quantify spectral difference.

Comparison of quantitative spectral similarity analysis methods for protein higher-order structure confirmation.

Optical and vibrational spectroscopic techniques are important tools for evaluating secondary and tertiary structures of proteins. These spectroscopic techniques are routinely applied in biopharmaceutical development to elucidate structural characteristics of protein products, to evaluate the impact of processing and storage conditions on product quality, and to assess comparability of a protein product before and after manufacturing changes. Conventionally, the degree of similarity between two spectra has been determined visually. In addition to requiring a significant amount of analyst training and experience, visual inspection of spectra is inherently subjective, and any determination of comparability based on visual analysis of spectra is therefore arbitrary. Here, we discuss a general methodology for evaluating the suitability of numerical methods to calculate spectral similarity, and then we apply the methodology to compare four quantitative spectral similarity methods: the correlation coefficient, area of spectral overlap, derivative correlation algorithm, and spectral difference methods. While the most effective spectral similarity method may depend on the particular application, all four approaches are superior to visual evaluation, and each is suitable for assessing the degree of similarity between spectra.

In vitro stoichiometry of complexes between the soluble RANK ligand and the monoclonal antibody denosumab.

The in vitro binding stoichiometry of denosumab, an IgG2 fully human monoclonal therapeutic antibody, to RANK ligand was determined by multiple complementary size separation techniques with mass measuring detectors, including two solution-based techniques (size-exclusion chromatography with static light scattering detection and sedimentation velocity analytical ultracentrifugation) and a gas-phase analysis by electrospray ionization time-of-flight mass spectrometry from aqueous nondenaturing solutions. The stoichiometry was determined under defined conditions ranging from small excess RANK ligand to large excess denosumab (up to 40:1). High concentrations of denosumab relative to RANK ligand were studied because of their physiological relevance; a large excess of denosumab is anticipated in circulation for extended periods relative to much lower concentrations of free soluble RANKL. The studies revealed that an assembly including 3 denosumab antibody molecules bound to 2 RANKL trimers (3D2R) is the most stable complex in DPBS at 37 °C. This differs from the 1:1 binding stoichiometry reported for RANKL and osteoprotegerin (OPG), a soluble homodimeric decoy receptor which binds RANKL with high affinity. Denosumab and RANKL also formed smaller assemblies including 1 denosumab and 2 RANKL trimer molecules (1D2R) under conditions of excess RANKL, 3 denosumab molecules and 1 RANKL trimer (3D1R) under conditions of excess denosumab, and larger assemblies, but these intermediate species were only present at lower temperatures (4 °C), shortly after mixing denosumab and RANKL, and converted over time to the more stable 3D2R assembly.

Applications of differential scanning calorimetry for thermal stability analysis of proteins: qualification of DSC.

Differential scanning calorimetry (DSC) has been used to characterize protein thermal stability, overall conformation, and domain folding integrity by the biopharmaceutical industry. Recently, there have been increased requests from regulatory agencies for the qualification of characterization methods including DSC. Understanding the method precision can help determine what differences between samples are significant and also establish the acceptance criteria for comparability and other characterization studies. In this study, we identify the parameters for the qualification of DSC for thermal stability analysis of proteins. We use these parameters to assess the precision and sensitivity of DSC and demonstrate that DSC is suitable for protein thermal stability analysis for these purposes. Several molecules from different structural families were studied. The experiments and data analyses were performed by different analysts using different instruments at different sites. The results show that the (apparent) thermal transition midpoint (T(m)) values obtained for the same protein by same and different instruments and/or analysts are quite reproducible, and the profile similarity values obtained for the same protein from the same instrument are also high. DSC is an appropriate method for assessing protein thermal stability and conformational changes.

Applications of circular dichroism (CD) for structural analysis of proteins: qualification of near- and far-UV CD for protein higher order structural analysis.

Circular dichroism (CD) spectroscopy is routinely used in the biopharmaceutical industry to study the effects of manufacturing, formulation, and storage conditions on protein conformation and stability, and these results are often included in regulatory filings. In this context, the purpose of CD spectroscopy is often to verify that a change in the formulation or manufacturing process of a product has not produced a change in the conformation of a protein. A comparison of two or more spectra is often required to confirm that the protein's structure has been maintained. Traditionally, such comparisons have been qualitative in nature, based on visually inspecting the overlaid spectra. However, visual assessment is inherently subjective and therefore prone to error. Furthermore, recent requests from regulatory agencies to demonstrate the suitability of the CD spectroscopic method for the purpose of comparing spectra have highlighted the need to appropriately qualify CD spectroscopy for characterization of biopharmaceutical protein products. In this study, we use a numerical spectral comparison approach to establish the precision of the CD spectroscopic method and to demonstrate that it is suitable for protein structural characterization in numerous biopharmaceutical applications.

Qualification of FTIR spectroscopic method for protein secondary structural analysis.

Fourier transform infrared (FTIR) spectroscopy is widely used to study protein secondary structure both in solution and in the solid state. The FTIR spectroscopic method has also been employed as a characterization method by the biopharmaceutical industry to determine the higher order structure of protein therapeutics, and to determine if any changes in protein conformation have occurred as a result of changes to process, formulation, manufacture, and storage conditions. The results of these studies are often included in regulatory filings; when comparability is assessed, the comparison is often qualitative. To demonstrate that the method can be quantitative, and is suitable for these intended purposes, the precision and sensitivity of the FTIR method were evaluated. The results show that FTIR spectroscopic analysis is reproducible with suitable method precision, that is, spectral similarity of replicate measurements is greater than 90%. The method can detect secondary structural changes caused by pH and denaturant. The sensitivity of the method in detecting structural changes depends on the extent of the changes and their impact on the resulting spectral similarity and characteristic FTIR bands. The results of these assessments are described in this paper.

Flow cytometry: a promising technique for the study of silicone oil-induced particulate formation in protein formulations.

Subvisible particles in formulations intended for parenteral administration are of concern in the biopharmaceutical industry. However, monitoring and control of subvisible particulates can be complicated by formulation components, such as the silicone oil used for the lubrication of prefilled syringes, and it is difficult to differentiate microdroplets of silicone oil from particles formed by aggregated protein. In this study, we demonstrate the ability of flow cytometry to resolve mixtures comprising subvisible bovine serum albumin (BSA) aggregate particles and silicone oil emulsion droplets with adsorbed BSA. Flow cytometry was also used to investigate the effects of silicone oil emulsions on the stability of BSA, lysozyme, abatacept, and trastuzumab formulations containing surfactant, sodium chloride, or sucrose. To aid in particle characterization, the fluorescence detection capabilities of flow cytometry were exploited by staining silicone oil with BODIPY 493/503 and model proteins with Alexa Fluor 647. Flow cytometric analyses revealed that silicone oil emulsions induced the loss of soluble protein via protein adsorption onto the silicone oil droplet surface. The addition of surfactant prevented protein from adsorbing onto the surface of silicone oil droplets. There was minimal formation of homogeneous protein aggregates due to exposure to silicone oil droplets, although oil droplets with surface-adsorbed trastuzumab exhibited flocculation. The results of this study demonstrate the utility of flow cytometry as an analytical tool for monitoring the effects of subvisible silicone oil droplets on the stability of protein formulations.

Measuring low levels of protein aggregation by sedimentation velocity.

The required performance of an analytical method depends on the purpose for which it will be used. As a methodology matures, it may find new application, and the performance demands placed on the method can increase. Sedimentation velocity analytical ultracentrifugation (SV-AUC) has a long and distinguished history with important contributions to molecular biology. Now the technique is transitioning into industrial settings, and among them, SV-AUC is now used to quantify the amount of protein aggregation in biopharmaceutical protein products, often at levels less than 1% of the total protein mass. In this paper, we review recent advances to SV methodology which have been shown to improve quantitation of protein aggregation. Then we discuss the performance of the SV method in its current state, with emphasis on the precision and quantitation limit of the method, in the context of existing industrial guidance on analytical method performance targets for quantitative methods.

Precision of protein aggregation measurements by sedimentation velocity analytical ultracentrifugation in biopharmaceutical applications.

Sedimentation velocity analytical ultracentrifugation (SV-AUC) is routinely applied in biopharmaceutical development to measure levels of protein aggregation in protein products. SV-AUC is free from many limitations intrinsic to size exclusion chromatography (SEC) such as mobile phase and column interaction effects on protein self-association. Despite these clear advantages, SV-AUC exhibits lower precision measurements than corresponding measurements by SEC. The precision of SV-AUC is influenced by numerous factors, including sample characteristics, cell alignment, centerpiece quality, and data analysis approaches. In this study, we evaluate the precision of SV-AUC in its current practice utilizing a multilaboratory, multiproduct intermediate precision study. We then explore experimental approaches to improve SV-AUC measurement precision, with emphasis on utilization of high quality centerpieces.

Detection of protein aggregates by sedimentation velocity analytical ultracentrifugation (SV-AUC): sources of variability and their relative importance.

Sedimentation velocity analytical ultracentrifugation (SV-AUC) has found application in the biopharmaceutical industry as a method of detecting and quantifying protein aggregates. While the technique offers several advantages (i.e., matrix-free separation and minimal sample handling), its results exhibit a high degree of variability relative to orthogonal size-sensitive separation techniques such as size exclusion chromatography (SEC). The goal of this work is to characterize and quantify the sources of variability that affect SV-AUC results, particularly size distributions for a monoclonal antibody monomer/dimer system. Contributions of individual factors to the overall variability are examined. Results demonstrate that alignment of sample cells to the center of rotation is the most significant contributing factor to overall variability. The relative importance of other factors (e.g., temperature equilibration, time-invariant noise, meniscus misplacement, etc.) are quantified and discussed.