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Oligomeric assemblies of the amyloid β peptide (Aβ) have been investigated for over two decades as possible neurotoxic agents in Alzheimer's disease. However, due to their heterogeneous and transient nature, it is not yet fully established which of the structural features of these oligomers may generate cellular damage. Here, we study distinct oligomer species formed by Aβ40 (the 40-residue form of Aβ) in the presence of four different metal ions (Al 3+ , Cu 2+ , Fe 2+ , and Zn 2+ ) and show that they differ in their structure and toxicity in human neuroblastoma cells. We then describe a correlation between the size of the oligomers and their neurotoxic activity, which provides a type of structure-toxicity relationship for these Aβ40 oligomer species. These results provide insight into the possible role of metal ions in Alzheimer's disease by the stabilization of Aβ oligomers.

Engineered antibody formats, such as antibody fragments and bispecifics, have the potential to offer improved therapeutic efficacy compared to traditional full-length monoclonal antibodies (mAbs). However, the translation of these non-natural molecules into successful therapeutics can be hampered by developability challenges. Here, we systematically analyzed 64 different antibody constructs targeting Tumor Necrosis Factor (TNF) which cover 8 distinct molecular format families, encompassing full-length antibodies, various types of single chain variable fragments, and bispecifics. We measured 15 biophysical properties related to activity, manufacturing, and stability, scoring variants with a flag-based risk approach and a recent in silico developability profiler. Our comparative assessment revealed that overall developability is higher for the natural full-length antibody format. Bispecific antibodies, antibodies with scFv fragments at the C-terminus of the light chain, and single-chain Fv antibody fragments (scFvs) have intermediate developability properties, while more complicated formats, such as scFv- scFv, bispecific mAbs with one Fab exchanged with a scFv, and diabody formats are collectively more challenging. In particular, our study highlights the propensity for fragmentation and aggregation, both in bulk and at interfaces, for many current engineered formats.

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 ( k D ), which is a measure of self-association under dilute conditions. 1 However, k D 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.

The clinical outcome of SARS-CoV-2 infections, which can range from asymptomatic to lethal, is crucially shaped by the concentration of antiviral antibodies and by their affinity to their targets. However, the affinity of polyclonal antibody responses in plasma is difficult to measure. Here we used microfluidic antibody affinity profiling (MAAP) to determine the aggregate affinities and concentrations of anti-SARS-CoV-2 antibodies in plasma samples of 42 seropositive individuals, 19 of which were healthy donors, 20 displayed mild symptoms, and 3 were critically ill. We found that dissociation constants, K d , of anti-receptor-binding domain antibodies spanned 2.5 orders of magnitude from sub-nanomolar to 43 nM. Using MAAP we found that antibodies of seropositive individuals induced the dissociation of pre-formed spike-ACE2 receptor complexes, which indicates that MAAP can be adapted as a complementary receptor competition assay. By comparison with cytopathic effect-based neutralisation assays, we show that MAAP can reliably predict the cellular neutralisation ability of sera, which may be an important consideration when selecting the most effective samples for therapeutic plasmapheresis and tracking the success of vaccinations., (© 2021 Schneider et al.)

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.

Prions consist of pathological aggregates of cellular prion protein and have the ability to replicate, causing neurodegenerative diseases, a phenomenon mirrored in many other diseases connected to protein aggregation, including Alzheimer's and Parkinson's diseases. However, despite their key importance in disease, the individual processes governing this formation of pathogenic aggregates, as well as their rates, have remained challenging to elucidate in vivo. Here we bring together a mathematical framework with kinetics of the accumulation of prions in mice and microfluidic measurements of aggregate size to dissect the overall aggregation reaction into its constituent processes and quantify the reaction rates in mice. Taken together, the data show that multiplication of prions in vivo is slower than in in vitro experiments, but efficient when compared with other amyloid systems, and displays scaling behavior characteristic of aggregate fragmentation. These results provide a framework for the determination of the mechanisms of disease-associated aggregation processes within living organisms.

Circular dichroism spectroscopy has become a powerful tool to characterise proteins and other biomolecules. For heterogeneous samples such as those present for interacting proteins, typically only average spectroscopic features can be resolved. Here we overcome this limitation by using free-flow microfluidic size separation in-line with synchrotron radiation circular dichroism to resolve the secondary structure of each component of a model protein mixture containing monomers and fibrils. To enable this objective, we have integrated far-UV compatible measurement chambers into PDMS-based microfluidic devices. Two architectures are proposed so as to accommodate for a wide range of concentrations. The approach, which can be used in combination with other bulk measurement techniques, paves the way to the study of complex mixtures such as the ones associated with protein misfolding and aggregation diseases including Alzheimer's and Parkinson's diseases.

Determination of individual rate constants for enzyme-catalyzed reactions is central to the understanding of their mechanism of action and is commonly obtained by stopped-flow kinetic experiments. However, most natural substrates either do not fluoresce/absorb or lack a significant change in their spectra while reacting and, therefore, are frequently chemically modified to render adequate molecules for their spectroscopic detection. Here, isothermal titration calorimetry (ITC) was used to obtain Michaelis-Menten plots for the trypsin-catalyzed hydrolysis of several substrates at different temperatures (278-318K): four spectrophotometrically blind lysine and arginine N-free esters, one N-substituted arginine ester, and one amide. A global fitting of these data provided the individual rate constants and activation energies for the acylation and deacylation reactions, and the ratio of the formation and dissociation rates of the enzyme-substrate complex, leading also to the corresponding free energies of activation. The results indicate that for lysine and arginine N-free esters deacylation is the rate-limiting step, but for the N-substituted ester and the amide acylation is the slowest step. It is shown that ITC is able to produce quality kinetic data and is particularly well suited for those enzymatic reactions that cannot be measured by absorption or fluorescence spectroscopy., (Copyright © 2015 Elsevier Inc. All rights reserved.)