Hamster neogenin, a host-cell protein contained in a respiratory syncytial virus candidate vaccine, induces antibody responses in rabbits but not in clinical trial participants.

A recombinant respiratory syncytial virus (RSV) fusion glycoprotein candidate vaccine (RSV-PreF) manufactured in Chinese hamster ovary cells was developed for immunization of pregnant women, to protect newborns against RSV disease through trans-placental antibody transfer. Traces of a host-cell protein, hamster neogenin (haNEO1), were identified in purified RSV-PreF antigen material. Given the high amino-acid sequence homology between haNEO1 and human neogenin (huNEO1), there was a risk that potential vaccine-induced anti-neogenin immunity could affect huNEO1 function in mother or fetus. Anti-huNEO1 IgGs were measured by enzyme-linked immunosorbent assay in sera from rabbits and trial participants (Phase 1 and 2 trials enrolling 128 men and 500 non-pregnant women, respectively; NCT01905215/NCT02360475) collected after immunization with RSV-PreF formulations containing different antigen doses with/without aluminum-hydroxide adjuvant. In rabbits, four injections administered at 14-day intervals induced huNEO1-specific IgG responses in an antigen-dose- and adjuvant-dependent manner, which plateaued in the highest-dose groups after three injections. In humans, no vaccination-induced anti-huNEO1 IgG responses were detected upon a single immunization, as the values in vaccine and control groups fluctuated around pre-vaccination levels up to 90/360 days post-vaccination. A minority of participants had anti-huNEO1 levels ≥ assay cutoff before vaccination, which did not increase post-vaccination. Thus, despite detecting vaccine-induced huNEO1-specific responses in rabbits, we found no evidence that the candidate vaccine had induced anti-huNEO1 immunity in human adults. The antigen purification process was nevertheless optimized, and haNEO1-reduced vaccines were used in a subsequent Phase 2 trial enrolling 400 non-pregnant women (NCT02956837), in which again no vaccine-induced anti-huNEO1 responses were detected.

The Diffusion of Hydrogen Peroxide Into the Liquid Product During Filling Operations Inside Vaporous Hydrogen Peroxide-Sterilized Isolators Can Be Predicted by a Mechanistic Model.

Isolators are commonly used in filling operations of pharmaceutical products. To ensure an aseptic inner environment, isolators are regularly sterilized with vaporized hydrogen peroxide. However, despite extensive purging with air, some residual H2O2 remains within the isolator atmosphere and may thus end up in the liquid pharmaceutical drug product, which subsequently may cause oxidation and impact the product's safety and efficacy. We aimed to evaluate the extent of this phenomenon and to model it. For that purpose, we studied the diffusion of H2O2 into water contained in small recipients exposed to the atmosphere of a H2O2-sterilized small-scale test isolator. Based on the results, a mechanistic model was proposed to estimate the quantity of H2O2 in the product, taking into account the time, filling volume, H2O2 concentration, and a configuration factor. Afterward, this model was challenged by filling water at a manufacturing scale, and we observed that the diffusion model could predict the trend of increasing H2O2 concentration. However, a consistent difference in H2O2 concentration between the model and the experimental results was observed, suggesting the contribution of another parameter. Our results can be used to predict more accurately H2O2 concentration in a pharmaceutical product at the manufacturing level.

Weighted conditional random fields for supervised interpatient heartbeat classification.

This paper proposes a method for the automatic classification of heartbeats in an ECG signal. Since this task has specific characteristics such as time dependences between observations and a strong class unbalance, a specific classifier is proposed and evaluated on real ECG signals from the MIT arrhythmia database. This classifier is a weighted variant of the conditional random fields classifier. Experiments show that the proposed method outperforms previously reported heartbeat classification methods, especially for the pathological heartbeats.