Characterisation of protein aggregation with the Smoluchowski coagulation approach for use in biopharmaceuticals

Understanding and controlling the molecular mechanisms of protein aggregation in mAb therapeutics

In antibody development and manufacturing, protein aggregation is a common challenge that can lead to serious efficacy and safety issues. To mitigate this problem, it is important to investigate its molecular origins. This review discusses (1) our current molecular understanding and theoretical models of antibody aggregation, (2) how various stress conditions related to antibody upstream and downstream bioprocesses can trigger aggregation, and (3) current mitigation strategies employed towards inhibiting aggregation. We discuss the relevance of the aggregation phenomenon in the context of novel antibody modalities and highlight how in silico approaches can be exploited to mitigate it.

A theoretical modeling framework for motile and colonial harmful algae

Climate change is leading to an increase in severity, frequency, and distribution of harmful algal blooms across the globe. For many harmful algae species in eutrophic lakes, the formation of such blooms is controlled by three factors: the lake hydrodynamics, the vertical motility of the algae organisms, and the ability of the organisms to form colonies. Here, using the common cyanobacterium Microcystis aeruginosa as an example, we develop a model that accounts for both vertical transport and colony dynamics. At the core of this treatment is a model for aggregation. For this, we used Smoluchowski dynamics containing parameters related to Brownian motion, turbulent shear, differential setting, and cell‐to‐cell adhesion. To arrive at a complete description of bloom formation, we place the Smoluchowski treatment as a reaction term in a set of one‐dimensional advection‐diffusion equations, which account for the vertical motion of the algal cells through molecular and turbulent diffusion and self‐regulating buoyant motion. Results indicate that Smoluchowski aggregation qualitatively describes the colony dynamics of M. aeruginosa. Further, the model demonstrates wind‐induced mixing is the dominant aggregation process, and the rate of aggregation is inversely proportional to algal concentration. Because blooms of Microcystis typically consist of large colonies, both of these findings have direct consequences to harmful algal bloom formation. While the theoretical framework outlined in this manuscript was derived for M. aeruginosa, both motility and colony formation are common among bloom‐forming algae. As such, this coupling of vertical transport and colony dynamics is a useful step for improving forecasts of surface harmful algal blooms.

Effects of Monovalent Salt on Protein-Protein Interactions of Dilute and Concentrated Monoclonal Antibody Formulations

In this study, we used sodium chloride (NaCl) to extensively modulate non-specific protein-protein interactions (PPI) of a humanized anti-streptavidin monoclonal antibody class 2 molecule (ASA-IgG2). The changes in PPI with varying NaCl (C_NaCl) and monoclonal antibody (mAb) concentration (C_mAb) were assessed using the diffusion interaction parameter k_D and second virial coefficient B₂₂ measured from solutions with low to moderate C_mAb. The effective structure factor S(q)_eff measured from concentrated mAb solutions using small-angle X-ray and neutron scattering (SAXS/SANS) was also used to characterize the PPI. Our results found that the nature of net PPI changed not only with C_NaCl, but also with increasing C_mAb. As a result, parameters measured from dilute and concentrated mAb samples could lead to different predictions on the stability of mAb formulations. We also compared experimentally determined viscosity results with those predicted from interaction parameters, including k_D and S(q)_eff. The lack of a clear correlation between interaction parameters and measured viscosity values indicates that the relationship between viscosity and PPI is concentration-dependent. Collectively, the behavior of flexible mAb molecules in concentrated solutions may not be correctly predicted using models where proteins are considered to be uniform colloid particles defined by parameters derived from low C_mAb.

Computational models for studying physical instabilities in high concentration biotherapeutic formulations

Computational prediction of the behavior of concentrated protein solutions is particularly advantageous in early development stages of biotherapeutics when material availability is limited and a large set of formulation conditions needs to be explored. This review provides an overview of the different computational paradigms that have been successfully used in modeling undesirable physical behaviors of protein solutions with a particular emphasis on high-concentration drug formulations. This includes models ranging from all-atom simulations, coarse-grained representations to macro-scale mathematical descriptions used to study physical instability phenomena of protein solutions such as aggregation, elevated viscosity, and phase separation. These models are compared and summarized in the context of the physical processes and their underlying assumptions and limitations. A detailed analysis is also given for identifying protein interaction processes that are explicitly or implicitly considered in the different modeling approaches and particularly their relations to various formulation parameters. Lastly, many of the shortcomings of existing computational models are discussed, providing perspectives and possible directions toward an efficient computational framework for designing effective protein formulations.

Rational Design of Protein-Specific Folding Modifiers

Protein-folding can go wrong in vivo and in vitro, with significant consequences for the living organism and the pharmaceutical industry, respectively. Here we propose a design principle for small-peptide-based protein-specific folding modifiers. The principle is based on constructing a "xenonucleus", which is a prefolded peptide that mimics the folding nucleus of a protein. Using stopped-flow kinetics, NMR spectroscopy, Förster resonance energy transfer, single-molecule force measurements, and molecular dynamics simulations, we demonstrate that a xenonucleus can make the refolding of ubiquitin faster by 33 ± 5%, while variants of the same peptide have little or no effect. Our approach provides a novel method for constructing specific, genetically encodable folding catalysts for suitable proteins that have a well-defined contiguous folding nucleus.

Long-term stability predictions of therapeutic monoclonal antibodies in solution using Arrhenius-based kinetics

Long-term stability of monoclonal antibodies to be used as biologics is a key aspect in their development. Therefore, its possible early prediction from accelerated stability studies is of major interest, despite currently being regarded as not sufficiently robust. In this work, using a combination of accelerated stability studies (up to 6 months) and first order degradation kinetic model, we are able to predict the long-term stability (up to 3 years) of multiple monoclonal antibody formulations. More specifically, we can robustly predict the long-term stability behaviour of a protein at the intended storage condition (5 °C), based on up to six months of data obtained for multiple quality attributes from different temperatures, usually from intended (5 °C), accelerated (25 °C) and stress conditions (40 °C). We have performed stability studies and evaluated the stability data of several mAbs including IgG1, IgG2, and fusion proteins, and validated our model by overlaying the 95% prediction interval and experimental stability data from up to 36 months. We demonstrated improved robustness, speed and accuracy of kinetic long-term stability prediction as compared to classical linear extrapolation used today, which justifies long-term stability prediction and shelf-life extrapolation for some biologics such as monoclonal antibodies. This work aims to contribute towards further development and refinement of the regulatory landscape that could steer toward allowing extrapolation for biologics during the developmental phase, clinical phase, and also in marketing authorisation applications, as already established today for small molecules.

Biopharmaceutical quality control with mass spectrometry

Mass spectrometry (MS) is a powerful technique for protein identification, quantification and characterization that is widely applied in biochemical studies, and which can provide data on the quantity, structural integrity and post-translational modifications of proteins. It is therefore a versatile and widely used analytic tool for quality control of biopharmaceuticals, especially in quantifying host-cell protein impurities, identifying post-translation modifications and structural characterization of biopharmaceutical proteins. Here, we summarize recent advances in MS-based analyses of these key quality attributes of the biopharmaceutical development and manufacturing processes.

Application of Tunable Resistive Pulse Sensing for the Quantification of Submicron Particles in Pharmaceutical Monoclonal Antibody Preparations

Tunable resistive pulse sensing (TRPS, qNano Gold, IZON Ltd.) was investigated as a method to quantify submicron particles (SMPs) between 0.1 and 1 µm in solutions of biopharmaceuticals. To reduce sample dilution, a spiking-in approach was used to add the appropriate amount of electrolytes required for the measurement. For correct particle quantification, an electrolyte concentration of at least 50 mM sodium chloride was needed. Intra- and inter-nanopore variability were below 5% for size and below 10% for concentration measurements when analyzing polystyrene standard beads. Submicron particle counts in a stir stressed IgG1 monoclonal antibody formulation resulted in a non-symmetrical, almost bell-shaped size distribution with a maximum at 250 nm when using a NP300 nanopore (IZON Ltd.). It was shown that particle counts are heavily underestimated below 250 nm, and therefore it is recommended to quantify particle counts by TRPS in samples with heterogeneous particle size distributions (e.g., biopharmaceuticals) only starting from the maximum of the histogram towards the upper limit of detection.

Two timescales control the creation of large protein aggregates in cells

Protein aggregation is of particular interest because of its connection with many diseases and disorders. Many factors can alter the dynamics and result of this process, one of them being the diffusivity of the monomers and aggregates in the system. Here, we study experimentally and theoretically an aggregation process in cells, and we identify two distinct physical timescales that set the number and size of aggregates. The first timescale involves fast aggregation of small clusters freely diffusing in the cytoplasm, whereas in the second one, the aggregates are larger than the pore size of the cytoplasm and thus barely diffuse, and the aggregation process is slowed down. However, the process is not entirely halted, potentially reflecting a myriad of active but random forces that stir the aggregates. Such a slow timescale is essential to account for the experimental results of the aggregation process. These results could also have implications in other processes of spatial organization in cell biology, such as phase-separated droplets.

Oral peptide delivery: challenges and the way ahead

Peptides and proteins have emerged as potential therapeutic agents and, in the search for the best treatment regimen, the oral route has been extensively evaluated because of its non-invasive and safe nature. The physicochemical properties of peptides and proteins along with the hurdles in the gastrointestinal tract (GIT), such as degrading enzymes and permeation barriers, are challenges to their delivery. To address these challenges, several conventional and novel approaches, such as nanocarriers, site-specific and stimuli specific delivery, are being used. In this review, we discuss the challenges to the oral delivery of peptides and the approaches used to tackle these challenges.

Oil-Immersion Flow Imaging Microscopy for Quantification and Morphological Characterization of Submicron Particles in Biopharmaceuticals

Flow imaging microscopy (FIM) is widely used to analyze subvisible particles starting from 2 μm in biopharmaceuticals. Recently, an oil-immersion FIM system emerged, the FlowCam Nano, designed to enable the characterization of particle sizes even below 2 μm. The aim of our study was to evaluate oil-immersion FIM (by using FlowCam Nano) in comparison to microfluidic resistive pulse sensing and resonant mass measurement for sizing and counting of particles in the submicron range. Polystyrene beads, a heat-stressed monoclonal antibody formulation and a silicone oil emulsion, were measured to assess the performance on biopharmaceutical relevant samples, as well as the ability to distinguish particle types based on instrument-derived morphological parameters. The determination of particle sizes and morphologies suffers from inaccuracies due to a low image contrast of small particles and light-scattering effects. The ill-defined measured volume impairs an accurate concentration determination. Nevertheless, FlowCam Nano in its current design complements the limited toolbox of submicron particle analysis of biopharmaceuticals by providing particle images in a size range that was previously not accessible with commercial FIM instruments.

Development of a Simple Kinetic Mathematical Model of Aggregation of Particles or Clustering of Receptors

The process of clustering of plasma membrane receptors in response to their agonist is the first step in signal transduction. The rate of the clustering process and the size of the clusters determine further cell responses. Here we aim to demonstrate that a simple 2-differential equation mathematical model is capable of quantitative description of the kinetics of 2D or 3D cluster formation in various processes. Three mathematical models based on mass action kinetics were considered and compared with each other by their ability to describe experimental data on GPVI or CR3 receptor clustering (2D) and albumin or platelet aggregation (3D) in response to activation. The models were able to successfully describe experimental data without losing accuracy after switching between complex and simple models. However, additional restrictions on parameter values are required to match a single set of parameters for the given experimental data. The extended clustering model captured several properties of the kinetics of cluster formation, such as the existence of only three typical steady states for this system: unclustered receptors, receptor dimers, and clusters. Therefore, a simple kinetic mass-action-law-based model could be utilized to adequately describe clustering in response to activation both in 2D and in 3D.

Stability Analysis of a Bulk-Surface Reaction Model for Membrane Protein Clustering

Protein aggregation on the plasma membrane (PM) is of critical importance to many cellular processes such as cell adhesion, endocytosis, fibrillar conformation, and vesicle transport. Lateral diffusion of protein aggregates or clusters on the surface of the PM plays an important role in governing their heterogeneous surface distribution. However, the stability behavior of the surface distribution of protein aggregates remains poorly understood. Therefore, understanding the spatial patterns that can emerge on the PM solely through protein-protein interaction, lateral diffusion, and feedback is an important step toward a complete description of the mechanisms behind protein clustering on the cell surface. In this work, we investigate the pattern formation of a reaction-diffusion model that describes the dynamics of a system of ligand-receptor complexes. The purely diffusive ligand in the cytosol can bind receptors in the PM and the resultant ligand-receptor complexes not only diffuse laterally but can also form clusters resulting in different oligomers. Finally, the largest oligomers recruit ligands from the cytosol using positive feedback. From a methodological viewpoint, we provide theoretical estimates for diffusion-driven instabilities of the protein aggregates based on the Turing mechanism. Our main result is a threshold phenomenon, in which a sufficiently high recruitment of ligands promotes the input of new monomeric components and consequently drives the formation of a single-patch spatially heterogeneous steady state.

Surfaces Affect Screening Reliability in Formulation Development of Biologics

PURPOSE

The ability to predict an antibody's propensity for aggregation is particularly important during product development to ensure the quality and safety of therapeutic antibodies. We demonstrate the role of container surfaces on the aggregation process of three mAbs under elevated temperature and long-term storage conditions in the absence of mechanical stress.

METHODS

A systematic study of aggregation is performed for different proteins, vial material, storage temperature, and presence of surfactant. We use size exclusion chromatography and micro-flow imaging to determine the bulk concentration of aggregates, which we combine with optical and atomic force microscopy of vial surfaces to determine the effect of solid-liquid interfaces on the bulk aggregate concentration under different conditions.

RESULTS

We show that protein particles under elevated temperature conditions adhere to the vial surfaces, causing a substantial underestimation of aggregation propensity as determined by common methods used in development of biologics. Under actual long-term storage conditions at 5°C, aggregate particles do not adhere to the surface, causing an increase in bulk concentration of particles, which cannot be predicted from elevated temperature screening tests by common methods alone. We also identify specific protein - surface interactions which promote oligomer formation in the nanometre range.

CONCLUSIONS

Special care should be taken when interpreting size exclusion and particle count data from stability studies if different temperatures and vial types are involved. We propose a novel combination of methods to characterise vial surfaces and bulk solution for a full understanding of protein aggregation processes in a sample.

Status of antiviral therapeutics against rabies virus and related emerging lyssaviruses

Rabies virus (RABV) constitutes a major social and economic burden associated with 60 000 deaths annually worldwide. Although pre-exposure and post-exposure treatment options are available, they are efficacious only when initiated before the onset of clinical symptoms. Aggravating the problem, the current RABV vaccine does not cross-protect against the emerging zoonotic phylogroup II lyssaviruses. A requirement for an uninterrupted cold chain and high cost of the immunoglobulin component of rabies prophylaxis generate an unmet need for the development of RABV-specific antivirals. We discuss desirable anti-RABV drug profiles, past efforts to address the problem and inhibitor candidates identified, and examine how the rapidly expanding structural insight into RABV protein organization has illuminated novel druggable target candidates and paved the way to structure-aided drug optimization. Special emphasis is given to the viral RNA-dependent RNA polymerase complex as a promising target for direct-acting broad-spectrum RABV inhibitors.

On the Reproducibility of Early-Stage Thermally Induced and Contact-Stir-Induced Protein Aggregation

Is aggregation kinetics for a protein under given conditions reproducible? Is aggregation inherently deterministic, stochastic, or even chaotic? Because protein aggregation in ex vivo formulations is complex, with many origins and manifestations, the question of aggregation reproducibility for a given protein, formulation, and stressor is of both fundamental and practical significance. This work concerns temperature-induced and contact-stir-induced aggregation of bovine serum albumin (BSA) and a monoclonal antibody (mAbX). It assesses reproducibility via early-stage aggregation rates (ARs) from light scattering. "Global stressors" affect the entire protein population, for example, temperature. "Local stressors" affect only a partial population at a given instant, for example, stirring. The instrumental error distribution (IED) allows stochasticity to be identified for AR distributions (ARDs) broader than IED. For ARD at the limit of the IED, the behavior is "minimally stochastic" or "operationally deterministic." A stochastic index is defined in terms of the ratio of the standard deviation (SD) of log(AR) data and the SD of IED. Thermal aggregation was operationally deterministic for BSA and mAbX, although significant lot-to-lot variations for BSA were found. ARD from contact-stir-stress was stochastic for BSA and mAb. Despite this, log(AR) decreases logarithmically with rpm. These trends may hold for other global and local stressors.