Protein degradation via aggregation route is a common source of SVPs. Well-established methods for SVP analysis, such as light obscuration and microflow imaging, are not high-throughput and require significant amounts of sample volume. The focus of this work was to evaluate the backgrounded membrane imaging (BMI) for SVP analysis and identify critical experimental parameters. In conclusion, BMI can be used as a high-throughput method for qualitative particle analysis.

The AQS3pro system, built upon microfluidic modulation spectroscopy, is an automated infrared platform optimized to determine a biomolecule’s secondary structure in a complex buffer and at formulation concentrations.  The AQS3pro combines a 24 or 96-well plate, a quantum cascade laser, and a microfluidic flow cell to produce highly precise, higher order structure measurements for the determination of stability, aggregation, lot-to-lot similarity, and formulation optimization of biomolecules.

Quantitative, robust counting of protein aggregates in biotherapeutics continues to be an important priority as a critical quality attribute. It is well known that due to the small index of refraction difference between protein aggregates and the buffer solution, that aggregate particles can be wrongly sized or missed altogether by methods that use imaging or light scattering as detection methods.  We will show a case study of how the new NIST ETFE standard reference material can be used to help calibrate measurements. We will also show how aggregates made from fluorescently labeled NISTmAb can be used in simultaneous fluorescence/brightfield imaging and flow cytometry measurements data to estimate how many particles are being in the brightfield imaging or light scattering measurements. The effects become increasingly important when the particle diameter is < 2 mm.

Particles remain a number one issue and reason for product recalls because they are critical for patient safety. Thus, sub-visible particle testing is mandatory for any bio-pharmaceutical preparation. Until today the commonly used technology comprises many manual steps and destroys the sample during testing. We show how little modifications can tailor the mature light obscuration technology to the biotech needs, allowing scientists only requiring small sample volumes while staying compliant to the regulations. It has the potential to save millions worth of precious sample material every year.

Real-time analytical techniques provide an opportunity for more effective monitoring and control of protein aggregation in bioprocessing as well as a better understanding of the mechanisms of protein aggregation. The objective of this presentation is to share these opportunities as well as challenges in the implementation of novel technologies that can aid in the detection of protein aggregates in real-time.

High molecular weight (HMW) species a size-variant purity attribute of therapeutic monoclonal antibodies, is of potential concern due to its possible impact on immune safety and efficacy. We demonstrate no measurable impact by HMW enriched from drug substance from a typical monoclonal antibody. No response was detected in model immune-cell lines or primary human immune cells exposed to the HMW. The HMW did not cause statistical changes in potency. Secondary and tertiary structures were comparable to monomers. These evidences suggests that HMW poses little or no impact on the immune safety or efficacy of the studied monoclonal antibody drug product.

The presentation will overview impurity analytical technologies and highlight analytical control strategy in the context of regulatory filing.

Monitoring impurities levels throughout bioprocess workflows is critical to quality. In this talk we will present data from different case studies in which biotherapeutic bioprocess samples and viral vector samples were assessed for process related impurities using the automated Gyrolab immunoassay system. Results demonstrate dynamic range, precision, dilutional linearity, and spike data that fulfills key regulatory bioanalytic method requirements with increased productivity in downstream process development.

Inherent particle formation and detection has become a topic of increased scrutiny in biopharmaceutical formulations. These particles, derived from degradation products of the API and/or the excipients, can be difficult to identify and characterize due to their fragility. In this talk, a case study will be presented on the characterization of these particles under a variety of different conditions with multiple analytical techniques.

Characterization of particles for injectable therapeutics is increasingly expected to establish product stability and minimize patient risk. We combined machine learning with imaging methods to develop ‘particle profiles’ customized to individual therapeutics, thereby allowing variations to be monitored. Case studies using closed-system transfer devices (CSTD) and cell-based therapeutics will be presented.

Identification of mAbs with low levels of self-association has traditionally required relatively concentrated protein solutions, confounding identification of such molecules during early antibody discovery and engineering. We introduce charge-stabilized self-interaction nanoparticle spectroscopy (CS-SINS), a colormetric assay that measures colloidal self-interaction of antibodies in ultra-dilute solutions (0.01 mg/mL). CS-SINS is predictive of high concentration properties such as viscosity and opalescence, which only emerge at orders of magnitude higher concentrations (100+ mg/mL).

In this presentation, I will discuss how the reversibility of protein unfolding is related to protein aggregation. Furthermore, I will introduce you to straightforward analytical approaches for studying unfolding reversibility to select aggregation-resistant antibodies and formulations that impede protein aggregation during storage. The presented concepts and techniques can be used for developability assessment and early-stage formulation development of various proteins.

NIST reference proteins were thermomechanically stressed in sodium azide solutions. Characterization of aggregates by field flow fractionation with light scattering and UV spectrometry showed time-dependent increase in aggregate amount and molar mass. Reducing gel electrophoresis revealed reversible aggregation paths. Kinetic modeling of data implied aggregation mechanism (with azide present) starts by nucleated growth then aggregate-aggregate condensation. Thus, azide preservatives used in ex vivo experiments have demonstrable effects on protein aggregation. 

The interaction of monoclonal antibodies (mAbs) with air/water interfaces plays a crucial role for the manufacture, formulation and storage of these high molecular weight molecules. This talk will focus on the molecular-scale behavior of mAbs that defines an orientational phase space during different stages of adsorption of mAbs to the air/water interface using experimental tools (pendant bubble tensiometry and X-ray reflectivity measurements) and homology modeling. Additionally, competitive adsorption process between mAbs and surfactant based excipients will be discussed to determine the concentration of surfactant necessary to prevent significant mAb adsorption.

Predictive models that evaluate aggregation behaviors during early-stage design can facilitate drug development. Machine learning is a widely used tool to train models that predict data with different attributes. However, most machine learning techniques require more data than is typically available in antibody development. In this work, we describe a rational feature selection framework to develop accurate models with a small number of features.