Lower Limb Joint Mechanics during Maximal Accelerative and Decelerative Running.

INTRODUCTION: Maximal acceleration and deceleration tasks are frequently required in team sports, often occurring rapidly in response to external stimuli. Accelerating and decelerating can be associated with lower limb injuries, thus knowledge of joint mechanics during these tasks can improve the understanding of both human high performance and injury mechanisms. The current study investigated the fundamental differences in lower limb joint mechanics when accelerating and decelerating by directly comparing the hip, knee and ankle joint moments and work done between the two tasks. METHODS: Twenty participants performed maximal effort acceleration and deceleration trials, with three-dimensional marker trajectories and ground reaction forces collected simultaneously. Experimental data was combined with inverse dynamics analysis to compute joint moments and work. RESULTS: Net joint work for all lower limb joints was positive during acceleration, and negative during deceleration. This occurred due to significantly greater positive work production from the ankle and hip during acceleration, and significantly greater negative work production from all joints during deceleration. The largest contributions to positive work during acceleration came from the ankle, followed by the hip and knee joints; whilst the largest contributions to negative work during deceleration came from the knee and hip joints, followed by the ankle. Peak joint moments were significantly greater when decelerating compared to accelerating, except for the peak ankle plantarflexion and hip flexion moments which were significantly greater when accelerating. CONCLUSIONS: Our findings may help to guide training interventions which aim to enhance the performance of acceleration and deceleration tasks, whilst also mitigating the associated injury risk.

Nonspecificity fingerprints for clinical-stage antibodies in solution.

Monoclonal antibodies (mAbs) have successfully been developed for the treatment of a wide range of diseases. The clinical success of mAbs does not solely rely on optimal potency and safety but also require good biophysical properties to ensure a high developability potential. In particular, nonspecific interactions are a key developability parameter to monitor during discovery and development. Despite an increased focus on the detection of nonspecific interactions, their underlying physicochemical origins remain poorly understood. Here, we employ solution-based microfluidic technologies to characterize a set of clinical-stage mAbs and their interactions with commonly used nonspecificity ligands to generate nonspecificity fingerprints, providing quantitative data on the underlying physical chemistry. Furthermore, the solution-based analysis enables us to measure binding affinities directly, and we evaluate the contribution of avidity in nonspecific binding by mAbs. We find that avidity can increase the apparent affinity by two orders of magnitude. Notably, we find that a subset of these highly developed mAbs show nonspecific electrostatic interactions, even at physiological pH and ionic strength, and that they can form microscale particles with charge-complementary polymers. The group of mAb constructs flagged here for nonspecificity are among the worst performers in independent reports of surface and column-based screens. The solution measurements improve on the state-of-the-art by providing a stand-alone result for individual mAbs without the need to benchmark against cohort data. Based on our findings, we propose a quantitative solution-based nonspecificity score, which can be integrated in the development workflow for biological therapeutics and more widely in protein engineering.

Automated optimisation of solubility and conformational stability of antibodies and proteins.

Biologics, such as antibodies and enzymes, are crucial in research, biotechnology, diagnostics, and therapeutics. Often, biologics with suitable functionality are discovered, but their development is impeded by developability issues. Stability and solubility are key biophysical traits underpinning developability potential, as they determine aggregation, correlate with production yield and poly-specificity, and are essential to access parenteral and oral delivery. While advances for the optimisation of individual traits have been made, the co-optimization of multiple traits remains highly problematic and time-consuming, as mutations that improve one property often negatively impact others. In this work, we introduce a fully automated computational strategy for the simultaneous optimisation of conformational stability and solubility, which we experimentally validate on six antibodies, including two approved therapeutics. Our results on 42 designs demonstrate that the computational procedure is highly effective at improving developability potential, while not affecting antigen-binding. We make the method available as a webserver at www-cohsoftware.ch.cam.ac.uk.

Surface patches induce nonspecific binding and phase separation of antibodies.

Nonspecific interactions are a key challenge in the successful development of therapeutic antibodies. The tendency for nonspecific binding of antibodies is often difficult to reduce by rational design, and instead, it is necessary to rely on comprehensive screening campaigns. To address this issue, we performed a systematic analysis of the impact of surface patch properties on antibody nonspecificity using a designer antibody library as a model system and single-stranded DNA as a nonspecificity ligand. Using an in-solution microfluidic approach, we find that the antibodies tested bind to single-stranded DNA with affinities as high as KD = 1 µM. We show that DNA binding is driven primarily by a hydrophobic patch in the complementarity-determining regions. By quantifying the surface patches across the library, the nonspecific binding affinity is shown to correlate with a trade-off between the hydrophobic and total charged patch areas. Moreover, we show that a change in formulation conditions at low ionic strengths leads to DNA-induced antibody phase separation as a manifestation of nonspecific binding at low micromolar antibody concentrations. We highlight that phase separation is driven by a cooperative electrostatic network assembly mechanism of antibodies with DNA, which correlates with a balance between positive and negative charged patches. Importantly, our study demonstrates that both nonspecific binding and phase separation are controlled by the size of the surface patches. Taken together, these findings highlight the importance of surface patches and their role in conferring antibody nonspecificity and its macroscopic manifestation in phase separation.

Multidimensional Protein Solubility Optimization with an Ultrahigh-Throughput Microfluidic Platform.

Protein-based biologics are highly suitable for drug development as they exhibit low toxicity and high specificity for their targets. However, for therapeutic applications, biologics must often be formulated to elevated concentrations, making insufficient solubility a critical bottleneck in the drug development pipeline. Here, we report an ultrahigh-throughput microfluidic platform for protein solubility screening. In comparison with previous methods, this microfluidic platform can make, incubate, and measure samples in a few minutes, uses just 20 µg of protein (>10-fold improvement), and yields 10,000 data points (1000-fold improvement). This allows quantitative comparison of formulation excipients, such as sodium chloride, polysorbate, histidine, arginine, and sucrose. Additionally, we can measure how solubility is affected by the combinatorial effect of multiple additives, find a suitable pH for the formulation, and measure the impact of mutations on solubility, thus enabling the screening of large libraries. By reducing material and time costs, this approach makes detailed multidimensional solubility optimization experiments possible, streamlining drug development and increasing our understanding of biotherapeutic solubility and the effects of excipients.

Time-restricted eating improves measures of daily glycaemic control in people with type 2 diabetes.

AIMS: Examine the effect of 5 d/wk, 9-h time-restricted eating (TRE) protocol on 24-h glycaemic control in adults with type 2 diabetes (T2D). METHODS: Nineteen adults with T2D (10 F/9 M; 50 ± 9 y, HbA1c 7.6% (60 mmol/mol), BMI ∼34 kg/m2) completed a pre-post non-randomised trial comprising of a 2-wk Habitual monitoring period followed by 9-h (10:00-19:00 h) TRE for 4-wk. Glycaemic control was assessed via continuous glucose monitoring (CGM; for mean 24-h glucose concentrations, 24-h total area under the curve (AUC) and glucose variability metrics), with dietary records and physical activity monitoring. Changes in CGM measures, dietary intake and physical activity were assessed with linear mixed-effects models. RESULTS: TRE did not alter dietary energy intake, macronutrient composition or physical activity, but reduced the daily eating window (-2 h 35 min, P < 0.001). Compared to the Habitual period, 24-h glucose concentrations (mean, SD) and AUC decreased in the 4-wk TRE period (mean: -0.7 ± 1.2 mmol/L, P = 0.02; SD: -0.2 ± 0.3 mmol/L, P = 0.01; 24-h AUC: -0.9 ± 1.4 mmol/L⋅h-1 P = 0.01). During TRE, participants spent 10% more time in range (3.9-10.0 mmol/L; P = 0.02) and 10% less time above range (>10.0 mmol/L; P = 0.02). CONCLUSIONS: Adhering 5 d/wk. to 9-h TRE improved glycaemic control in adults with T2D, independent of changes in physical activity or dietary intake. CLINICAL TRIAL REGISTRATION: Australia New Zealand Clinical Trial Registry, ACTRN12618000938202.

AF4 and PEG Precipitation as Predictive Assays for Antibody Self-Association.

Monoclonal antibodies (mAbs) are often formulated as high-protein-concentration solutions, which in some cases can exhibit physical stability issues such as high viscosity and opalescence. To ensure that mAb-based drugs can meet their manufacturing, stability, and delivery requirements, it is advantageous to screen for and select mAbs during discovery that are not prone to such behaviors. It has been recently shown that both these macroscopic properties can be predicted to a certain extent from the diffusion interaction parameter (kD), which is a measure of self-association under dilute conditions.1 However, kD can be challenging to measure at the early stage of discovery, where a relatively large amount of a high-purity material, which is required by traditional methods, is often not available. In this study, we demonstrate asymmetric field-flow fractionation (AF4) as a tool to measure self-association and therefore identify antibodies with problematic issues at high concentrations. The principle lies on the ability to concentrate the sample close to the membrane during the injection mode, which can reach formulation-relevant concentrations (>100 mg/mL).2 By analyzing a well-characterized library of commercial antibodies, we show that the measured retention time of the antibodies allows us to pinpoint molecules that exhibit issues at high concentrations. Remarkably, our AF4 assay requires very little (30 µg) sample under dilute conditions and does not need extensive sample purification. Furthermore, we show that a polyethylene glycol (PEG) precipitation assay provides results consistent with AF4 and moreover can further differentiate molecules with issues of opalescence or high viscosity. Overall, our results delineate a two-step strategy for the identification of problematic variants at high concentrations, with AF4 for early developability screening, followed by a PEG assay to validate the problematic molecules and further discriminate between opalescence or high-viscosity issues. This two-step antibody selection strategy enables us to select antibodies early in the discovery process, which are compatible with high-concentration formulations.

Non-specificity as the sticky problem in therapeutic antibody development.

Antibodies are highly potent therapeutic scaffolds with more than a hundred different products approved on the market. Successful development of antibody-based drugs requires a trade-off between high target specificity and target binding affinity. In order to better understand this problem, we here review non-specific interactions and explore their fundamental physicochemical origins. We discuss the role of surface patches - clusters of surface-exposed amino acid residues with similar physicochemical properties - as inducers of non-specific interactions. These patches collectively drive interactions including dipole-dipole, π-stacking and hydrophobic interactions to complementary moieties. We elucidate links between these supramolecular assembly processes and macroscopic development issues, such as decreased physical stability and poor in vivo half-life. Finally, we highlight challenges and opportunities for optimizing protein binding specificity and minimizing non-specificity for future generations of therapeutics.

Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods.

Although antibodies have become the fastest-growing class of therapeutics on the market, it is still challenging to develop them for therapeutic applications, which often require these molecules to withstand stresses that are not present in vivo. We define developability as the likelihood of an antibody candidate with suitable functionality to be developed into a manufacturable, stable, safe, and effective drug that can be formulated to high concentrations while retaining a long shelf life. The implementation of reliable developability assessments from the early stages of antibody discovery enables flagging and deselection of potentially problematic candidates, while focussing available resources on the development of the most promising ones. Currently, however, thorough developability assessment requires multiple in vitro assays, which makes it labor intensive and time consuming to implement at early stages. Furthermore, accurate in vitro analysis at the early stage is compromised by the high number of potential candidates that are often prepared at low quantities and purity. Recent improvements in the performance of computational predictors of developability potential are beginning to change this scenario. Many computational methods only require the knowledge of the amino acid sequences and can be used to identify possible developability issues or to rank available candidates according to a range of biophysical properties. Here, we describe how the implementation of in silico tools into antibody discovery pipelines is increasingly offering time- and cost-effective alternatives to in vitro experimental screening, thus streamlining the drug development process. We discuss in particular the biophysical and biochemical properties that underpin developability potential and their trade-offs, review various in vitro assays to measure such properties or parameters that are predictive of developability, and give an overview of the growing number of in silico tools available to predict properties important for antibody development, including the CamSol method developed in our laboratory.

Measuring Self-Association of Antibody Lead Candidates with Dynamic Light Scattering.

In this method chapter, we provide a brief overview of the key methods available to measure self-association of monoclonal antibodies (mAbs) and explain for which experimental throughputs they are usually applied. We then focus on dynamic light scattering (DLS) and describe experimental details on how to measure the diffusion interaction parameter (kD) which is occasionally referred to as the gold standard for measuring self-association of proteins. The kD is a well-established parameter to predict solution viscosity, which is one of the most critical developability parameters of mAbs. Finally, we present a pH and excipient screen that is designed to measure self-association with DLS under conditions that are relevant for bioprocessing and formulation of mAbs. The presented light scattering methods are well suited for lead candidate selections where it is essential to select mAbs with high developability potential for progression toward first human dose.

You Can't Replicate What You Can't See: A Call for Researchers to Share Their Data and Avoid "Fragile" Correlations.

SYNOPSIS: We suggest that a measure of a correlation's fragility is the minimum number of items that, when replaced with the group median, result in a nonsignificant correlation on reanalysis. Between January 2000 and July 2021, there were 1769 significant correlations reported in 142 papers published in this journal, and only 51 correlations (2.9%) had available data (scatter plots from which we could digitize the raw data). Twenty-six of these 51 correlations were fragile at 4 or fewer replacements. Five of the reported significant correlations were not significant when we replicated the analysis from the extracted data. J Orthop Sports Phys Ther 2021;51(12):556-558. doi:10.2519/jospt.2021.0112.

Design of Biopharmaceutical Formulations Accelerated by Machine Learning.

In addition to activity, successful biological drugs must exhibit a series of suitable developability properties, which depend on both protein sequence and buffer composition. In the context of this high-dimensional optimization problem, advanced algorithms from the domain of machine learning are highly beneficial in complementing analytical screening and rational design. Here, we propose a Bayesian optimization algorithm to accelerate the design of biopharmaceutical formulations. We demonstrate the power of this approach by identifying the formulation that optimizes the thermal stability of three tandem single-chain Fv variants within 25 experiments, a number which is less than one-third of the experiments that would be required by a classical DoE method and several orders of magnitude smaller compared to detailed experimental analysis of full combinatorial space. We further show the advantage of this method over conventional approaches to efficiently transfer historical information as prior knowledge for the development of new biologics or when new buffer agents are available. Moreover, we highlight the benefit of our technique in engineering multiple biophysical properties by simultaneously optimizing both thermal and interface stabilities. This optimization minimizes the amount of surfactant in the formulation, which is important to decrease the risks associated with corresponding degradation processes. Overall, this method can provide high speed of converging to optimal conditions, the ability to transfer prior knowledge, and the identification of new nonlinear combinations of excipients. We envision that these features can lead to a considerable acceleration in formulation design and to parallelization of operations during drug development.

A factor VIIIa-mimetic bispecific antibody, Mim8, ameliorates bleeding upon severe vascular challenge in hemophilia A mice.

Hemophilia A is a bleeding disorder resulting from deficient factor VIII (FVIII), which normally functions as a cofactor to activated factor IX (FIXa) that facilitates activation of factor X (FX). To mimic this property in a bispecific antibody format, a screening was conducted to identify functional pairs of anti-FIXa and anti-FX antibodies, followed by optimization of functional and biophysical properties. The resulting bispecific antibody (Mim8) assembled efficiently with FIXa and FX on membranes, and supported activation with an apparent equilibrium dissociation constant of 16 nM. Binding affinity with FIXa and FX in solution was much lower, with equilibrium dissociation constant values for FIXa and FX of 2.3 and 1.5 µM, respectively. In addition, the activity of Mim8 was dependent on stimulatory activity contributed by the anti-FIXa arm, which enhanced the proteolytic activity of FIXa by 4 orders of magnitude. In hemophilia A plasma and whole blood, Mim8 normalized thrombin generation and clot formation, with potencies 13 and 18 times higher than a sequence-identical analogue of emicizumab. A similar potency difference was observed in a tail vein transection model in hemophilia A mice, whereas reduction of bleeding in a severe tail-clip model was observed only for Mim8. Furthermore, the pharmacokinetic parameters of Mim8 were investigated and a half-life of 14 days shown in cynomolgus monkeys. In conclusion, Mim8 is an activated FVIII mimetic with a potent and efficacious hemostatic effect based on preclinical data.

Screening for protein-protein interactions with asymmetrical flow field-flow fractionation.

We describe a new method for screening protein-protein interaction of biopharmaceutical molecules at dilute concentrations to predict development issues at high concentration. The method is based on Asymmetrical Flow Field-Flow Fractionation (AF4) measurements using well known effects of protein-protein attraction on the fractionation profile due to elevated protein concentrations occurring close to the membrane. We explore the effect for 4 different monoclonal antibodies and show that the profiles obtained are quite different. Interestingly, we find that the recovery in AF4 correlates with the diffusion interaction parameter, which is a standard method for the analysis of protein-protein attraction. The results are insensitive to the protein concentration and buffer composition of the sample solution and only depend on the absolute amount of protein loaded and on the running buffer. This makes the method highly suitable for developability assessment in a compound discovery workflow.

Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation.

Successful biologics must satisfy multiple properties including activity and particular physicochemical features that are globally defined as developability. These multiple properties must be simultaneously optimized in a very broad design space of protein sequences and buffer compositions. In this context, artificial intelligence (AI), and especially machine learning (ML), have great potential to accelerate and improve the optimization of protein properties, increasing their activity and safety as well as decreasing their development time and manufacturing costs. We highlight the emerging applications of ML in biologics discovery and development, focusing on protein engineering, early biophysical screening, and formulation. We discuss the power of ML in extracting information from complex datasets and in reducing the necessary experimental effort to simultaneously achieve multiple quality targets. We finally anticipate possible future interventions of AI in several steps of the biological landscape.

An accelerated surface-mediated stress assay of antibody instability for developability studies.

High physical stability is required for the development of monoclonal antibodies (mAbs) into successful therapeutic products. Developability assays are used to predict physical stability issues such as high viscosity and poor conformational stability, but protein aggregation remains a challenging property to predict. Among different types of stresses, air-water and solid-liquid interfaces are well known to potentially trigger protein instability and induce aggregation. Yet, in contrast to the increasing number of developability assays to evaluate bulk properties, there is still a lack of experimental methods to evaluate antibody stability against interfaces. Here, we investigate the potential of a hydrophobic nanoparticle surface-mediated stress assay to assess the stability of mAbs during the early stages of development. We evaluate this surface-mediated accelerated stability assay on a rationally designed library of 14 variants of a humanized IgG4, featuring a broad span of solubility values and other developability properties. The assay could identify variants characterized by high instability against agitation in the presence of air-water interfaces. Remarkably, for the set of investigated molecules, we observe strong correlations between the extent of aggregation induced by the surface-mediated stress assay and other developability properties of the molecules, such as aggregation upon storage at 45°C, self-association (evaluated by affinity-capture self-interaction nanoparticle spectroscopy) and nonspecific interactions (estimated by cross-interaction chromatography, stand-up monolayer chromatography (SMAC), SMAC*). This highly controlled surface-mediated stress assay has the potential to complement and increase the ability of the current set of screening techniques to assess protein aggregation and developability potential of mAbs during the early stages of drug development. Abbreviations:AC-SINS: Affinity-Capture Self-Interaction Nanoparticle Spectroscopy; AMS: Ammonium sulfate precipitation; ANS: 1-anilinonaphtalene-8-sulfonate; CIC: Cross-interaction chromatography; DLS: Dynamic light scattering; HIC: Hydrophobic interaction chromatography; HNSSA: Hydrophobic nanoparticles surface-stress assay; mAb: Monoclonal antibody; NP: Nanoparticle; SEC: Size exclusion chromatography; SMAC: Stand-up monolayer chromatography; WT: Wild type.

Improving the Developability of an Antigen Binding Fragment by Aspartate Substitutions.

Aggregation can be a major challenge in the development of antibody-based pharmaceuticals as it can compromise the quality of the product during bioprocessing, formulation, and drug administration. To avoid aggregation, developability assessment is often run in parallel with functional optimization in the early screening phases to flag and deselect problematic molecules. As developability assessment can be demanding with regard to time and resources, there is a high focus on the development of molecule design strategies for engineering molecules with a high developability potential. Previously, Dudgeon et al. [(2012) Proc. Natl. Acad. Sci. U. S. A. 109, 10879-10884] demonstrated how Asp substitutions at specific positions in human variable domains and single-chain variable fragments could decrease the aggregation propensity. Here, we have investigated whether these Asp substitutions would improve the developability potential of a murine antigen binding fragment (Fab). A full combinatorial library consisting of 393 Fab variants with single, double, and triple Asp substitutions was first screened in silico with Rosetta; thereafter, 26 variants with the highest predicted thermodynamic stability were selected for production. All variants were subjected to a set of developability studies. Interestingly, most variants had thermodynamic stability on par with or improved relative to that of the wild type. Twenty-five of the variants exhibited improved nonspecificity. Half of the variants exhibited improved aggregation resistance. Strikingly, while we observed remarkable improvement in the developability potential, the Asp substitutions had no substantial effect on the antigenic binding affinity. Altogether, by combining the insertion of negative charges and the in silico screen based on computational models, we were able to improve the developability of the Fab rapidly.

In vitro and in silico assessment of the developability of a designed monoclonal antibody library.

Despite major advances in antibody discovery technologies, the successful development of monoclonal antibodies (mAbs) into effective therapeutic and diagnostic agents can often be impeded by developability liabilities, such as poor expression, low solubility, high viscosity and aggregation. Therefore, strategies to predict at the early phases of antibody development the risk of late-stage failure of antibody candidates are highly valuable. In this work, we employ the in silico solubility predictor CamSol to design a library of 17 variants of a humanized mAb predicted to span a broad range of solubility values, and we examine their developability potential with a battery of commonly used in vitro and in silico assays. Our results demonstrate the ability of CamSol to rationally enhance mAb developability, and provide a quantitative comparison of in vitro developability measurements with each other and with more resource-intensive solubility measurements, as well as with in silico predictors that offer a potentially faster and cheaper alternative. We observed a strong correlation between predicted and experimentally determined solubility values, as well as with measurements obtained using a panel of in vitro developability assays that probe non-specific interactions. These results indicate that computational methods have the potential to reduce or eliminate the need of carrying out laborious in vitro quality controls for large numbers of lead candidates. Overall, our study provides support to the emerging view that the implementation of in silico tools in antibody discovery campaigns can ensure rapid and early selection of antibodies with optimal developability potential.

Potent α-Synuclein Aggregation Inhibitors, Identified by High-Throughput Screening, Mainly Target the Monomeric State.

α-Synuclein (αSN) aggregation is central to the etiology of Parkinson's disease (PD). Large-scale screening of compounds to identify aggregation inhibitors is challenged by stochastic αSN aggregation and difficulties in detecting early-stage oligomers (αSOs). We developed a high-throughput screening assay combining SDS-stimulated αSN aggregation with FRET to reproducibly detect initial stages in αSN aggregation. We screened 746,000 compounds, leading to 58 hits that markedly inhibit αSN aggregation and reduce αSOs' membrane permeabilization activity. The most effective aggregation inhibitors were derivatives of (4-hydroxynaphthalen-1-yl)sulfonamide. They interacted strongly with the N-terminal part of monomeric αSN and reduced αSO-membrane interactions, possibly by affecting electrostatic interactions. Several compounds reduced αSO toxicity toward neuronal cell lines. The inhibitors introduced chemical modifications of αSN that were, however, not a prerequisite for inhibitory activity. We also identified several phenyl-benzoxazol compounds that promoted αSN aggregation (proaggregators). These compounds may be useful tools to modulate αSN aggregation in cellula.

Antibodies against the C-terminus of α-synuclein modulate its fibrillation.

The 140-residue natively disordered protein α-synuclein (aSN) is a central component in the development of a family of neurodegenerative diseases termed synucleinopathies. This is attributed to its ability to form cytotoxic aggregates such as oligomers and amyloid fibrils. Consequently there have been intense efforts to avoid aggregation or reroute the aggregation pathway using pharmaceutical agents such as small molecules, chaperones and antibodies. aSN's lack of persistent structure in the monomeric state, as well as the multitude of different oligomeric and even different fibrillar states, makes it difficult to raise antibodies that would be efficacious in neutralizing all conformations of aSN. However, the C-terminal 20-40 residues of aSN are a promising epitope for antibody development. It is primarily disordered in both monomeric and aggregated forms, and an anti-C-terminal antibody will therefore be able to bind all forms. Furthermore, it might not interfere with the folding of aSN into membranes, which could be important for its physiological role. Here we report a screen of a series of monoclonal antibodies, which all target the C-terminal of aSN. According to dot blot analyses, different antibodies bound different forms of aSN with different preferences and showed reduced binding to monomeric compared to aggregated (oligomeric and fibrillary) aSN. Consequently they have different effects on aSN's ability to fibrillate and permeabilize membranes. Generally, the antibodies with strongest binding to aggregated aSN in dot blot, also inhibited fibrillation and membrane permeabilization the most, and promoted formation of amorphous aggregates surrounded by small and thin fibers. This suggests that the development of antibodies that targets the C-terminus, exposed in the aggregated forms of aSN, may be beneficial for improved immunotherapy against PD.