Anti-influenza Hyperimmune Immunoglobulin Enhances Fc-functional Antibody Immunity during Human Influenza Infection

BACKGROUND: New treatments for severe influenza are needed. Passive transfer of influenza-specific hyperimmune pooled immunoglobulin (Flu-IVIG) boosts neutralising antibody responses to past strains in influenza-infected subjects. The effect of Flu-IVIG on antibodies with Fc-mediated functions, which may target diverse influenza strains, is unclear. METHODS: We studied the capacity of Flu-IVIG, relative to standard IVIG, to bind to Fc receptors and mediate antibody-dependent cellular cytotoxicity in vitro. The effect of Flu-IVIG infusion, compared to placebo infusion, was examined in serial plasma samples from 24 subjects with confirmed influenza infection in the INSIGHT FLU005 pilot study. RESULTS: Flu-IVIG contains higher concentrations of Fc-functional antibodies than IVIG against a diverse range of influenza hemagglutinins. Following infusion of Flu-IVIG into influenza-infected subjects, a transient increase in Fc-functional antibodies was present for 1-3 days against infecting and non-infecting strains of influenza. CONCLUSIONS: Flu-IVIG contains antibodies with Fc-mediated functions against influenza virus and passive transfer of Flu-IVIG increases anti-influenza Fc-functional antibodies in the plasma of influenza-infected subjects. Enhancement of Fc-functional antibodies to a diverse range of influenza strains suggests that Flu-IVIG infusion could prove useful in the context of novel influenza virus infections, when there may be minimal or no neutralising antibodies in the Flu-IVIG preparation

Antiviral susceptibility of clade 2.3.4.4b highly pathogenic avian influenza A(H5N1) viruses isolated from birds and mammals in the United States, 2022

Clade 2.3.4.4b highly pathogenic avian influenza (HPAI) A(H5N1) viruses that are responsible for devastating outbreaks in birds and mammals pose a potential threat to public health. Here, we evaluated their susceptibility to influenza antivirals. Of 1,015 sequences of HPAI A(H5N1) viruses collected in the United States during 2022, eight viruses (∼0.8%) had a molecular marker of drug resistance to an FDA-approved antiviral: three adamantane-resistant (M2-V27A), four oseltamivir-resistant (NA-H275Y), and one baloxavir-resistant (PA-I38T). Additionally, 31 viruses contained mutations that may reduce susceptibility to inhibitors of neuraminidase (NA) (n = 20) or cap-dependent endonuclease (CEN) (n = 11). A panel of 22 representative viruses was tested phenotypically. Overall, clade 2.3.4.4b A(H5N1) viruses lacking recognized resistance mutations were susceptible to FDA-approved antivirals. Oseltamivir was least potent at inhibiting NA activity, while the investigational NA inhibitor AV5080 was most potent, including against NA mutants. A novel NA substitution T438N conferred 12-fold reduced inhibition by zanamivir, and in combination with the known marker N295S, synergistically affected susceptibility to all five NA inhibitors. In cell culture-based assays HINT and IRINA, the PA-I38T virus displayed 75- to 108-fold and 37- to 78-fold reduced susceptibility to CEN inhibitors, baloxavir and the investigational AV5116, respectively. Viruses with PA-I38M or PA-A37T showed 5- to 10-fold reduced susceptibilities. As HPAI A(H5N1) viruses continue to circulate and evolve, close monitoring of drug susceptibility is needed for risk assessment and to inform decisions regarding antiviral stockpiling

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Fine control of chlorophyll-carotenoid interactions defines the functionality of light-harvesting proteins in plants

V.B. and C.D.P.D. acknowledge the support from the Leverhulme Trust RPG-2015-337. This research utilized Queen Mary’s MidPlus computational facilities, supported by QMUL Research-IT and funded by EPSRC grant EP/K000128/1. W.P.B acknowledges support from the Photosynthetic Antenna Research Center (PARC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award DE-SC0001035 for initial development of the TDC calculation code, as well as support from Army Research Office (ARO-MURI) Award W911NF1210420 for further development