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)].

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.

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.

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.

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.

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.