Lessons Learned from the Development and Roll-Out of the rVSVΔG-ZEBOV-GP Zaire ebolavirus Vaccine to Inform Marburg Virus and Sudan ebolavirus Vaccines.

This review describes key aspects of the development of the rVSVΔG-ZEBOV-GP Ebola vaccine and key activities which are continuing to further expand our knowledge of the product. Extensive partnerships and innovative approaches were used to address the various challenges encountered during this process. The rVSVΔG-ZEBOV-GP Ebola vaccine was initially approved by the European Medicines Agency and prequalified by the World Health Organization in November 2019. It was approved by the United States Food and Drug Administration in December 2019 and approved in five African countries within 90 days of prequalification. The development resulted in the first stockpile of a registered Ebola vaccine that is available to support outbreak response. This also provides insights into how the example of rVSVΔG-ZEBOV-GP can inform the development of vaccines for Sudan ebolavirus, Marburg virus, and other emerging epidemic diseases in terms of the types of approaches and data needed to support product registration, availability, and the use of a filovirus vaccine.

Environmental Risk Assessment for rVSVΔG-ZEBOV-GP, a Genetically Modified Live Vaccine for Ebola Virus Disease.

rVSVΔG-ZEBOV-GP is a live, attenuated, recombinant vesicular stomatitis virus (rVSV)-based vaccine for the prevention of Ebola virus disease caused by Zaire ebolavirus. As a replication-competent genetically modified organism, rVSVΔG-ZEBOV-GP underwent various environmental evaluations prior to approval, the most in-depth being the environmental risk assessment (ERA) required by the European Medicines Agency. This ERA, as well as the underlying methodology used to arrive at a sound conclusion about the environmental risks of rVSVΔG-ZEBOV-GP, are described in this review. Clinical data from vaccinated adults demonstrated only infrequent, low-level shedding and transient, low-level viremia, indicating a low person-to-person infection risk. Animal data suggest that it is highly unlikely that vaccinated individuals would infect animals with recombinant virus vaccine or that rVSVΔG-ZEBOV-GP would spread within animal populations. Preclinical studies in various hematophagous insect vectors showed that these species were unable to transmit rVSVΔG-ZEBOV-GP. Pathogenicity risk in humans and animals was found to be low, based on clinical and preclinical data. The overall risk for non-vaccinated individuals and the environment is thus negligible and can be minimized further through defined mitigation strategies. This ERA and the experience gained are relevant to developing other rVSV-based vaccines, including candidates under investigation for prevention of COVID-19.

Comparison of the industrial source complex and AERMOD dispersion models: case study for human health risk assessment.

Air quality models are typically used to predict the fate and transport of air emissions from industrial sources to comply with federal and state regulatory requirements and environmental standards, as well as to determine pollution control requirements. For many years, the U.S. Environmental Protection Agency (EPA) widely used the Industrial Source Complex (ISC) model because of its broad applicability to multiple source types. Recently, EPA adopted a new rule that replaces ISC with AERMOD, a state-of-the-practice air dispersion model, in many air quality impact assessments. This study compared the two models as well as their enhanced versions that incorporate the Plume Rise Model Enhancements (PRIME) algorithm. PRIME takes into account the effects of building downwash on plume dispersion. The comparison used actual point, area, and volume sources located on two separate facilities in conjunction with site-specific terrain and meteorological data. The modeled maximum total period average ground-level air concentrations were used to calculate potential health effects for human receptors. The results show that the switch from ISC to AERMOD and the incorporation of the PRIME algorithm tend to generate lower concentration estimates at the point of maximum ground-level concentration. However, the magnitude of difference varies from insignificant to significant depending on the types of the sources and the site-specific conditions. The differences in human health effects, predicted using results from the two models, mirror the concentrations predicted by the models.