First-in-Human Study to Evaluate Safety, Tolerability, Pharmacokinetics, and Pharmacodynamics of a Rapidly Developed SARS-CoV-2 Therapeutic Antibody, AOD01, in Healthy Adults.

INTRODUCTION: AOD01 is a novel, fully human immunoglobulin (Ig) G1 neutralizing monoclonal antibody that was developed as a therapeutic against severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2). This first-in-human study assessed safety, tolerability, pharmacokinetics (PK), and pharmacodynamics of AOD01 in healthy volunteers. METHODS: Intravenous doses of AOD01 were evaluated in escalating cohorts [four single-dose cohorts (2, 5, 10, and 20 mg/kg) and one two-dose cohort (two doses of 20 mg/kg, 24 h apart)]. RESULTS: Twenty-three subjects were randomized to receive AOD01 or a placebo in blinded fashion. A total of 34 treatment-emergent adverse events (TEAEs) were reported; all were mild in severity. Related events (headache and diarrhea) were reported in one subject each. No event of infusion reactions, serious adverse event (SAE), or discontinuation due to AE were reported. The changes in laboratory parameters, vital signs, and electrocardiograms were minimal. Dose-related exposure was seen from doses 2 to 20 mg/kg as confirmed by Cmax and AUC0-tlast. The median Tmax was 1.5-3 h. Clearance was dose independent. Study results revealed long half-lives (163-465 h). Antidrug antibodies (ADA) to AOD01 were not detected among subjects, except in one subject of the two-dose cohort on day 92. Sustained ex vivo neutralization of SARS-CoV-2 was recorded until day 29 with single doses from 2 to 20 mg/kg and until day 43 with two doses of 20 mg/kg. CONCLUSIONS: AOD01 was safe and well tolerated, demonstrated dose-related PK, non-immunogenic status, and sustained ex vivo neutralization of SARS-CoV-2 after single intravenous dose ranging from 2 to 20 mg/kg and two doses of 20 mg/kg and show good potential for treatment of SARS-CoV-2 infection. (Health Sciences Authority identifier number CTA2000119).

Rationally targeted anti-VISTA antibody that blockades the C-C' loop region can reverse VISTA immune suppression and remodel the immune microenvironment to potently inhibit tumor growth in an Fc independent manner.

BACKGROUND: Despite significant progress in cancer immunotherapy in recent years, resistance to existing immune checkpoint therapies (ICT) is common. V-domain Ig suppressor of T cell activation (VISTA), a predominantly myeloid immune checkpoint regulator, represents a promising therapeutic target due to its role in suppressing proinflammatory antitumor responses in myeloid-enriched tumor microenvironments. However, uncertainty around the cognate VISTA ligand has made the development of effective anti-VISTA antibodies challenging. The expression of VISTA on normal immune cell subtypes argues for a neutralizing non-depleting antibody, however, previous reported anti-VISTA antibodies use IgG1 Fc isotypes that deplete VISTA+ cells by antibody dependent cellular cytotoxicity/complement dependent cytotoxicity and these antibodies have shown fast serum clearance and immune toxicities. METHOD: Here we used a rational antibody discovery approach to develop the first Fc-independent anti-VISTA antibody, HMBD-002, that binds a computationally predicted functional epitope within the C-C-loop, distinct from other known anti-VISTA antibodies. This epitope is species-conserved allowing robust in vitro and in vivo testing of HMBD-002 in human and murine models of immune activation and cancer including humanized mouse models. RESULTS: We demonstrate here that blockade by HMBD-002 inhibits VISTA binding to potential partners, including V-Set and Immunoglobulin domain containing 3, to reduce myeloid-derived suppression of T cell activity and prevent neutrophil migration. Analysis of immune cell milieu suggests that HMBD-002 treatment stimulates a proinflammatory phenotype characterized by a Th1/Th17 response, recapitulating a phenotype previously noted in VISTA knockout models. This mechanism of action is further supported by immune-competent syngenic and humanized mouse models of colorectal, breast and lung cancer where neutralizing VISTA, without depleting VISTA expressing cells, significantly inhibited tumor growth while decreasing infiltration of suppressive myeloid cells and increasing T cell activity. Finally, we did not observe either the fast serum clearance or immune toxicities that have been reported for IgG1 antibodies. CONCLUSION: In conclusion, we have shown that VISTA-induced immune suppression can be reversed by blockade of the functional C-C' loop region of VISTA with a first-in-class rationally targeted and non-depleting IgG4 isotype anti-VISTA antibody, HMBD-002. This antibody represents a highly promising novel therapy in the VISTA-suppressed ICT non-responder population.

The Fc-mediated effector functions of a potent SARS-CoV-2 neutralizing antibody, SC31, isolated from an early convalescent COVID-19 patient, are essential for the optimal therapeutic efficacy of the antibody.

Although SARS-CoV-2-neutralizing antibodies are promising therapeutics against COVID-19, little is known about their mechanism(s) of action or effective dosing windows. We report the generation and development of SC31, a potent SARS-CoV-2 neutralizing antibody, isolated from a convalescent patient. Antibody-mediated neutralization occurs via an epitope within the receptor-binding domain of the SARS-CoV-2 Spike protein. SC31 exhibited potent anti-SARS-CoV-2 activities in multiple animal models. In SARS-CoV-2 infected K18-human ACE2 transgenic mice, treatment with SC31 greatly reduced viral loads and attenuated pro-inflammatory responses linked to the severity of COVID-19. Importantly, a comparison of the efficacies of SC31 and its Fc-null LALA variant revealed that the optimal therapeutic efficacy of SC31 requires Fc-mediated effector functions that promote IFNγ-driven anti-viral immune responses, in addition to its neutralization ability. A dose-dependent efficacy of SC31 was observed down to 5mg/kg when administered before viral-induced lung inflammatory responses. In addition, antibody-dependent enhancement was not observed even when infected mice were treated with SC31 at sub-therapeutic doses. In SARS-CoV-2-infected hamsters, SC31 treatment significantly prevented weight loss, reduced viral loads, and attenuated the histopathology of the lungs. In rhesus macaques, the therapeutic potential of SC31 was evidenced through the reduction of viral loads in both upper and lower respiratory tracts to undetectable levels. Together, the results of our preclinical studies demonstrated the therapeutic efficacy of SC31 in three different models and its potential as a COVID-19 therapeutic candidate.

10D1F, an Anti-HER3 Antibody that Uniquely Blocks the Receptor Heterodimerization Interface, Potently Inhibits Tumor Growth Across a Broad Panel of Tumor Models.

In recent years, HER3 has increasingly been implicated in the progression of a variety of tumor types and in acquired resistance to EGFR and HER2 therapies. Whereas EGFR and HER2 primarily signal through the MAPK pathway, HER3, as a heterodimer with EGFR or HER2, potently activates the PI3K pathway. Despite its critical role, previous attempts to target HER3 with neutralizing antibodies have shown disappointing efficacy in the clinic, most likely due to suboptimal and indirect mechanisms of action that fail to completely block heterodimerization; for example, tumors can escape inhibition of ligand binding by upregulating ligand-independent mechanisms of HER3 activation. We therefore developed 10D1F, a picomolar affinity, highly specific anti-HER3 neutralizing antibody that binds the HER3 heterodimerization interface, a region that was hitherto challenging to raise antibodies against. We demonstrate that 10D1F potently inhibits both EGFR:HER3 and HER2:HER3 heterodimerization to durably suppress activation of the PI3K pathway in a broad panel of tumor models. Even as a monotherapy, 10D1F shows superior inhibition of tumor growth in the same cell lines both in vitro and in mouse xenograft experiments, when compared with other classes of anti-HER3 antibodies. This includes models demonstrating ligand-independent activation of heterodimerization as well as constitutively activating mutations in the MAPK pathway. Possessing favorable pharmacokinetic and toxicologic profiles, 10D1F uniquely represents a new class of anti-HER3 neutralizing antibodies with a novel mechanism of action that offers significant potential for broad clinical benefit.10D1F is a novel anti-HER3 antibody that uniquely binds the receptor dimerization interface to block ligand-dependent and independent heterodimerization with EGFR/HER2 and thus more potently inhibits tumor growth than existing anti-HER3 antibodies.

An integrative model of ion regulation in yeast.

Yeast cells are able to tolerate and adapt to a variety of environmental stresses. An essential aspect of stress adaptation is the regulation of monovalent ion concentrations. Ion regulation determines many fundamental physiological parameters, such as cell volume, membrane potential, and intracellular pH. It is achieved through the concerted activities of multiple cellular components, including ion transporters and signaling molecules, on both short and long time scales. Although each component has been studied in detail previously, it remains unclear how the physiological parameters are maintained and regulated by the concerted action of all components under a diverse range of stress conditions. In this study, we have constructed an integrated mathematical model of ion regulation in Saccharomyces cerevisiae to understand this coordinated adaptation process. Using this model, we first predict that the interaction between phosphorylated Hog1p and Tok1p at the plasma membrane inhibits Tok1p activity and consequently reduces Na(+) influx under NaCl stress. We further characterize the impacts of NaCl, sorbitol, KCl and alkaline pH stresses on the cellular physiology and the differences between the cellular responses to these stresses. We predict that the calcineurin pathway is essential for maintaining a non-toxic level of intracellular Na(+) in the long-term adaptation to NaCl stress, but that its activation is not required for maintaining a low level of Na(+) under other stresses investigated. We provide evidence that, in addition to extrusion of toxic ions, Ena1p plays an important role, in some cases alongside Nha1p, in re-establishing membrane potential after stress perturbation. To conclude, this model serves as a powerful tool for both understanding the complex system-level properties of the highly coordinated adaptation process and generating further hypotheses for experimental investigation.

The degree distribution of networks: statistical model selection.

The degree distribution has been viewed as an important characteristic of network data. Many biological networks have been labelled scale-free as their degree distribution can be approximately described by a power-law probability distribution. This chapter presents a formal statistical model selection procedure that can determine which functional form, from a collection of specified models, best describes the degree distribution of network data. The degree distribution found for empirical data is viewed as belonging to a class of probability models and the model which best describes the data is determined in a maximum likelihood framework. In conclusion, it is important to note that these statistical tests do not confirm the true underlying distribution of the observed data, but instead show which models from a chosen set best describe the data. In reality, these approaches should be viewed as providing evidence for which probability models do not adequately (or optimally) describe the data, and give an indication of the underlying sampling and true interaction properties of the system considered.

Nonidentifiability of the source of intrinsic noise in gene expression from single-burst data.

Over the last few years, experimental data on the fluctuations in gene activity between individual cells and within the same cell over time have confirmed that gene expression is a "noisy" process. This variation is in part due to the small number of molecules taking part in some of the key reactions that are involved in gene expression. One of the consequences of this is that protein production often occurs in bursts, each due to a single promoter or transcription factor binding event. Recently, the distribution of the number of proteins produced in such bursts has been experimentally measured, offering a unique opportunity to study the relative importance of different sources of noise in gene expression. Here, we provide a derivation of the theoretical probability distribution of these bursts for a wide variety of different models of gene expression. We show that there is a good fit between our theoretical distribution and that obtained from two different published experimental datasets. We then prove that, irrespective of the details of the model, the burst size distribution is always geometric and hence determined by a single parameter. Many different combinations of the biochemical rates for the constituent reactions of both transcription and translation will therefore lead to the same experimentally observed burst size distribution. It is thus impossible to identify different sources of fluctuations purely from protein burst size data or to use such data to estimate all of the model parameters. We explore methods of inferring these values when additional types of experimental data are available.

Network motifs: structure does not determine function.

BACKGROUND: A number of publications have recently examined the occurrence and properties of the feed-forward motif in a variety of networks, including those that are of interest in genome biology, such as gene networks. The present work looks in some detail at the dynamics of the bi-fan motif, using systems of ordinary differential equations to model the populations of transcription factors, mRNA and protein, with the aim of extending our understanding of what appear to be important building blocks of gene network structure. RESULTS: We develop an ordinary differential equation model of the bi-fan motif and analyse variants of the motif corresponding to its behaviour under various conditions. In particular, we examine the effects of different steady and pulsed inputs to five variants of the bifan motif, based on evidence in the literature of bifan motifs found in Saccharomyces cerevisiae (commonly known as baker's yeast). Using this model, we characterize the dynamical behaviour of the bi-fan motif for a wide range of biologically plausible parameters and configurations. We find that there is no characteristic behaviour for the motif, and with the correct choice of parameters and of internal structure, very different, indeed even opposite behaviours may be obtained. CONCLUSION: Even with this relatively simple model, the bi-fan motif can exhibit a wide range of dynamical responses. This suggests that it is difficult to gain significant insights into biological function simply by considering the connection architecture of a gene network, or its decomposition into simple structural motifs. It is necessary to supplement such structural information by kinetic parameters, or dynamic time series experimental data, both of which are currently difficult to obtain.