Protection of newborn macaques by plant-derived HIV broadly neutralizing antibodies: a model for passive immunotherapy during breastfeeding

Preventing HIV infection in newborns by vertical transmission remains an important unmet medical need in resource poor areas where ART is not available and mothers and infants cannot be treated pre-partum or during the breastfeeding period. In the present study, the protective efficacy of the potent HIV neutralizing antibodies PGT121 and VRC07-523, both produced in plants, were assessed in a multiple SHIV challenge breastfeeding macaque model. Newborn macaques received either six weekly SC injections with PGT121 alone or as a cocktail of PGT121-LS+VRC07-523-LS injected three times every two weeks. Viral challenge with SHIV SF162P3 was twice weekly over 5.5 weeks using 11 exposures. Despite the transient presence of plasma viral RNA either immediately after the first challenge or as single-point blips, the antibodies prevented a productive infection in all babies with no sustained plasma viremia, compared to viral loads ranging from 10 3 to 5x10 8 in four untreated controls. No virus was detected in PBMCs and only 3/159 tissue samples were weakly positive in the treated babies. Newborn macaques proved to be immunocompetent, producing transient anti-Env antibodies and anti-drug antibody (ADA) that were maintained in the circulation after passive bnAbs clearance. ADA responses were directed to the IgG1 Fc CH2-CH3 domains, which has not been observed to date in adult monkeys passively treated with PGT121 or VRC01. In addition, high levels of VRC07-523 anti-idiotypic antibodies in the circulation of one newborn was concomitant with the rapid elimination of VRC07. Plant-expressed antibodies show promise as passive immunoprophylaxis in a breastfeeding model in newborns. Importance Plant-produced human neutralizing antibody prophylaxis is highly effective in preventing infection in newborn monkeys during repeated oral exposure, modeling virus in breastmilk, and offers advantages in cost of production and safety. These findings raise the possibility that anti-Env antibodies may contribute to the control of viral replication in this newborn model and the observed immune responsiveness may be driven by the long-lived presence of immune complexes.

Incorporating Exposure Measurement Data from Similar Exposure Scenarios to Inform Exposure Modeling Estimates: A Demonstration Using Cluster Analysis and Bayesian Modeling

Addressing occupational health and safety concerns early in the design stage anticipates hazards and enables health professionals to recommend control measures that can best protect workers’ health. This method is a well-established tool in public health. Importantly, its success depends on a comprehensive exposure assessment that incorporates previous exposure data and outcomes. Traditional methods for characterizing similar occupational exposure scenarios rely on expert judgment or qualitative descriptions of relevant exposure data, which often include undisclosed underlying assumptions about specific exposure conditions. Thus, improved methods for predicting exposure modeling estimates based on available data are needed. This study proposes that cluster analysis can be used to quantify the relevance of existing exposure scenarios that are similar to a new scenario. We demonstrate how this method improves exposure predictions. Exposure data and contextual information of the scenarios were collected from past exposure assessment reports. Prior distributions for the exposure distribution parameters were specified using Stoffenmanager® 8 predictions. Gower distance and k-Medoids clustering algorithm analyses grouped existing scenarios into clusters based on similarity. The information was used in a Bayesian model to specify the degree of correlation between similar scenarios and the scenarios to be assessed. Using the distance metric to characterize the degree of similarity, the performance of the Bayesian model was improved in terms of the average bias of model estimates and measured data, reducing from 0.77 (SD: 2.0) to 0.49 (SD: 1.8). Nevertheless, underestimation of exposures still occurred for some rare scenarios, which tended to be those with highly variable exposure data. In conclusion, the cluster analysis approach may enable transparent selection of similar exposure scenarios for factoring into design-phase assessments and thereby improve exposure modeling estimates.

Variable resolution probabilistic modeling of residential exposure and vulnerability for risk applications

In risk assessment, the exposure component describes the elements exposed to the natural hazards and susceptible to damage or loss, while the vulnerability component defines the likelihood to incur damage or loss conditional on a given level of hazard intensity. In this article, we propose a novel adaptive approach to exposure modeling which exploits Dirichlet-Multinomial Bayesian updating to implement the incremental assimilation of sparse in situ survey data into probabilistic models described by compositions (proportions). This methodology is complemented by the introduction of a custom spatial aggregation support based on variable-resolution Central Voronoidal Tessellations. The proposed methodology allows for a more consistent integration of empirical observations, typically from engineering surveys, into large-scale models that can also efficiently exploit expert-elicited knowledge. The resulting models are described in a probabilistic framework, and as such allow for a more thorough analysis of the underlying uncertainty. The proposed approach is applied and discussed in five countries in Central Asia.