Characterization of Aggregated Antibody-Silicone Oil Complexes: From Perspectives of Morphology, 3D Image, and Fcγ Receptor Activation

From cell factories to patients: Stability challenges in biopharmaceuticals manufacturing and administration with mitigation strategies

Active ingredients of biopharmaceuticals consist of a wide array of biomolecular structures, including those of enzymes, monoclonal antibodies, nucleic acids, and recombinant proteins. Recently, these molecules have dominated the pharmaceutical industry owing to their safety and efficacy. However, their manufacturing is hindered by high cost, inadequate batch-to-batch equivalence, inherent instability, and other quality issues. This article is an up-to-date review of the challenges encountered during different stages of biopharmaceutical production and mitigation of problems arising during their development, formulation, manufacturing, and administration. It is a broad overview discussion of stability issues encountered during product life cycle i.e., upstream processing (aggregation, solubility, host cell proteins, color change), downstream bioprocessing (aggregation, fragmentation), formulation, manufacturing, and delivery to patients.

Characterizing Silicone Oil-Induced Protein Aggregation with Stimulated Raman Scattering Imaging

Particles in biopharmaceutical products present high risks due to their detrimental impacts on product quality and safety. Identification and quantification of particles in drug products are important to understand particle formation mechanisms, which can help develop control strategies for particle formation during the formulation development and manufacturing process. However, existing analytical techniques such as microflow imaging and light obscuration measurement lack the sensitivity and resolution to detect particles with sizes smaller than 2 μm. More importantly, these techniques are not able to provide chemical information to determine particle composition. In this work, we overcome these challenges by applying the stimulated Raman scattering (SRS) microscopy technique to monitor the C-H Raman stretching modes of the proteinaceous particles and silicone oil droplets formed in the prefilled syringe barrel. By comparing the relative signal intensity and spectral features of each component, most particles can be classified as protein-silicone oil aggregates. We further show that morphological features are poor indicators of particle composition. Our method has the capability to quantify aggregation in protein therapeutics with chemical and spatial information in a label-free manner, potentially allowing high throughput screening or investigation of aggregation mechanisms.

Mechanism of Protein-PDMS Visible Particles Formation in Liquid Vial Monoclonal Antibody Formulation

Visible particles (VPs) formation in liquid monoclonal antibody formulations is a critical quality issue. Formulations that include poloxamer 188 (PX188) as a surfactant are prone to the formation of VPs comprising aggregated complexes of protein and polydimethylsiloxane (PDMS; silicone oil) derived from primary containers. However, the mechanisms through which these VPs form are complicated and remain to be fully elucidated. This study demonstrates for the first time the dominant spot and pathway of protein-PDMS VP formation in a particular liquid vial formulation. Specifically, when a vial sealed with a PDMS-coated stopper is stored in an upright position under conditions whereby the antibody solution has become well-adhered to the stopper and an air phase exists in the vicinity, protein-PDMS aggregates form on the stopper and are then desorbed into the drug solution to be detected as VPs. Here, we evaluated the effects of several factors on VP formation: adhesion of the drug solution to the stopper, storage orientation, silicone coating on the stopper, vial material, and hydrophobicity of PX188. Remarkably, we found that changing any one of the factors could significantly affect VP formation. Our findings are instructive for better understanding the mechanisms of VP formation in vial products and can provide strategies for VP mitigation in biotherapeutics.

Instability Challenges and Stabilization Strategies of Pharmaceutical Proteins

Maintaining the structure of protein and peptide drugs has become one of the most important goals of scientists in recent decades. Cold and thermal denaturation conditions, lyophilization and freeze drying, different pH conditions, concentrations, ionic strength, environmental agitation, the interaction between the surface of liquid and air as well as liquid and solid, and even the architectural structure of storage containers are among the factors that affect the stability of these therapeutic biomacromolecules. The use of genetic engineering, side-directed mutagenesis, fusion strategies, solvent engineering, the addition of various preservatives, surfactants, and additives are some of the solutions to overcome these problems. This article will discuss the types of stress that lead to instabilities of different proteins used in pharmaceutics including regulatory proteins, antibodies, and antibody-drug conjugates, and then all the methods for fighting these stresses will be reviewed. New and existing analytical methods that are used to detect the instabilities, mainly changes in their primary and higher order structures, are briefly summarized.

A Collaborative Study on the Classification of Silicone Oil Droplets and Protein Particles Using Flow Imaging Method

In this study, we conducted a collaborative study on the classification between silicone oil droplets and protein particles detected using the flow imaging (FI) method toward proposing a standardized classifier/model. We compared four approaches, including a classification filter composed of particle characteristic parameters, principal component analysis, decision tree, and convolutional neural network in the performance of the developed classifier/model. Finally, the points to be considered were summarized for measurement using the FI method, and for establishing the classifier/model using machine learning to differentiate silicone oil droplets and protein particles.