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.

SARS-CoV-2 Neutralizing Antibody Responses towards Full-Length Spike Protein and the Receptor-Binding Domain.

Tools to monitor SARS-CoV-2 transmission and immune responses are needed. We present a neutralization ELISA to determine the levels of Ab-mediated virus neutralization and a preclinical model of focused immunization strategy. The ELISA is strongly correlated with the elaborate plaque reduction neutralization test (ρ = 0.9231, p < 0.0001). The neutralization potency of convalescent sera strongly correlates to IgG titers against SARS-CoV-2 receptor-binding domain (RBD) and spike (ρ = 0.8291 and 0.8297, respectively; p < 0.0001) and to a lesser extent with the IgG titers against protein N (ρ = 0.6471, p < 0.0001). The preclinical vaccine NMRI mice models using RBD and full-length spike Ag as immunogens show a profound Ab neutralization capacity (IC50 = 1.9 × 104 to 2.6 × 104 and 3.9 × 103 to 5.2 × 103, respectively). Using a panel of novel high-affinity murine mAbs, we also show that a majority of the RBD-raised mAbs have inhibitory properties, whereas only a few of the spike-raised mAbs do. The ELISA-based viral neutralization test offers a time- and cost-effective alternative to the plaque reduction neutralization test. The immunization results indicate that vaccine strategies focused only on the RBD region may have advantages compared with the full spike.

SARS-CoV-2 Antibody Responses Are Correlated to Disease Severity in COVID-19 Convalescent Individuals.

Globally, the COVID-19 pandemic has had extreme consequences for the healthcare system and has led to calls for diagnostic tools to monitor and understand the transmission, pathogenesis, and epidemiology, as well as to evaluate future vaccination strategies. In this study, we have developed novel, to our knowledge, flexible ELISA-based assays for specific detection of human SARS-CoV-2 Abs against the receptor-binding domain, including an Ag sandwich ELISA relevant for large population screening and three isotype-specific assays for in-depth diagnostics. Their performance was evaluated in a cohort of 350 convalescent participants with previous COVID-19 infection, ranging from asymptomatic to critical cases. We mapped the Ab responses to different areas on protein N and S and showed that the IgM, A, and G Ab responses against receptor-binding domain are significantly correlated to the disease severity. These assays and the data generated from them are highly relevant for diagnostics and prognostics and contribute to the understanding of long-term COVID-19 immunity.

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.

The functional role of the autolysis loop in the regulation of factor X upon hemostatic response.

BACKGROUND: The regulation of factor X (FX) is critical to maintain the balance between blood coagulation and fluidity. OBJECTIVES: To functionally characterize the role of the FX autolysis loop in the regulation of the zymogen and active form of FX. METHODS: We introduced novel N-linked glycosylations on the surface-exposed loop spanning residues 143-150 (chymotrypsin numbering) of FX. The activity and inhibition of recombinant FX variants was quantified in pure component assays. The in vitro thrombin generation potential of the FX variants was evaluated in FX-depleted plasma. RESULTS: The factor VIIa (FVIIa)-mediated activation and prothrombin activation was reduced, presumably through steric hinderance. Prothrombin activation was, however, recovered in presence of cofactor factor Va (FVa) despite a reduced prothrombinase assembly. The introduced N-glycans exhibited position-specific effects on the interaction with two FXa inhibitors: tissue factor pathway inhibitor (TFPI) and antithrombin (ATIII). Ki for the inhibition by full-length TFPI of these FXa variants was increased by 7- to 1150-fold, whereas ATIII inhibition in the presence of the heparin-analog Fondaparinux was modestly increased by 2- to 15-fold compared with wild-type. When supplemented in zymogen form, the FX variants exhibited reduced thrombin generation activity relative to wild-type FX, whereas enhanced procoagulant activity was measured for activated FXa variants. CONCLUSION: The autolysis loop participates in all aspects of FX regulation. In plasma-based assays, a modest decrease in FX activation rate appeared to knock down the procoagulant response even when down regulation of FXa activity by inhibitors was reduced.

The alpha/B.1.1.7 SARS-CoV-2 variant exhibits significantly higher affinity for ACE-2 and requires lower inoculation doses to cause disease in K18-hACE2 mice.

The alpha/B.1.1.7 SARS-CoV-2 lineage emerged in autumn 2020 in the United Kingdom and transmitted rapidly until winter 2021 when it was responsible for most new COVID-19 cases in many European countries. The incidence domination was likely due to a fitness advantage that could be driven by the receptor-binding domain (RBD) residue change (N501Y), which also emerged independently in other variants of concern such as the beta/B.1.351 and gamma/P.1 strains. Here, we present a functional characterization of the alpha/B.1.1.7 variant and show an eightfold affinity increase towards human angiotensin-converting enzyme-2 (ACE-2). In accordance with this, transgenic hACE2 mice showed a faster disease progression and severity after infection with a low dose of B.1.1.7, compared to an early 2020 SARS-CoV-2 isolate. When challenged with sera from convalescent individuals or anti-RBD monoclonal antibodies, the N501Y variant showed a minor, but significant elevated evasion potential of ACE-2/RBD antibody neutralization. The data suggest that the single asparagine to tyrosine substitution remarkable rise in affinity may be responsible for the higher transmission rate and severity of the B.1.1.7 variant.

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.