Performance of Immunoglobulin G Serology on Finger Prick Capillary Dried Blood Spot Samples to Detect a SARS-CoV-2 Antibody Response.

We investigate the diagnostic accuracy and predictive value of finger prick capillary dried blood spot (DBS) samples tested by a quantitative multiplex anti-immunoglobulin G (IgG) assay to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies after infection or vaccination. This cross-sectional study involved participants (n = 6,841) from several serological surveys conducted in nonhospitalized children and adults throughout 2020 and 2021 in British Columbia (BC), Canada. Analysis used paired DBS and serum samples from a subset of participants (n = 642) prior to vaccination to establish signal thresholds and calculate diagnostic accuracy by logistic regression. Discrimination of the logistic regression model was assessed by receiver operator curve (ROC) analysis in an n = 2,000 bootstrap of the paired sample (n = 642). The model was cross-validated in a subset of vaccinated persons (n = 90). Unpaired DBS samples (n = 6,723) were used to evaluate anti-IgG signal distributions. In comparison to paired serum, DBS samples from an unvaccinated population possessed a sensitivity of 79% (95% confidence interval [95% CI]: 58 to 91%) and specificity of 97% (95% CI: 95 to 98%). ROC analysis found that DBS samples accurately classify SARS-CoV-2 seroconversion at an 88% percent rate (area under the curve [AUC] = 88% [95% CI: 80 to 95%]). In coronavirus disease 2019 (COVID-19) vaccine dose one or two recipients, the sensitivity of DBS testing increased to 97% (95% CI: 83 to 99%) and 100% (95% CI: 88 to 100%). Modeling found that DBS testing possesses a high positive predictive value (98% [95% CI: 97 to 98%]) in a population with 75% seroprevalence. We demonstrate that DBS testing should be considered to reliably detect SARS-CoV-2 seropositivity from natural infection or vaccination. IMPORTANCE Dried blood spot samples have comparable diagnostic accuracy to serum collected by venipuncture when tested by an electrochemiluminescent assay for antibodies and should be considered to reliably detect seropositivity following SARS-CoV-2 infection and/or vaccination.

Predicted Cellular Immunity Population Coverage Gaps for SARS-CoV-2 Subunit Vaccines and Their Augmentation by Compact Peptide Sets.

Subunit vaccines induce immunity to a pathogen by presenting a component of the pathogen and thus inherently limit the representation of pathogen peptides for cellular immunity-based memory. We find that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) subunit peptides may not be robustly displayed by the major histocompatibility complex (MHC) molecules in certain individuals. We introduce an augmentation strategy for subunit vaccines that adds a small number of SARS-CoV-2 peptides to a vaccine to improve the population coverage of pathogen peptide display. Our population coverage estimates integrate clinical data on peptide immunogenicity in convalescent COVID-19 patients and machine learning predictions. We evaluate the population coverage of 9 different subunits of SARS-CoV-2, including 5 functional domains and 4 full proteins, and augment each of them to fill a predicted coverage gap.

Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions.

We present a combinatorial machine learning method to evaluate and optimize peptide vaccine formulations for SARS-CoV-2. Our approach optimizes the presentation likelihood of a diverse set of vaccine peptides conditioned on a target human-population HLA haplotype distribution and expected epitope drift. Our proposed SARS-CoV-2 MHC class I vaccine formulations provide 93.21% predicted population coverage with at least five vaccine peptide-HLA average hits per person (≥ 1 peptide: 99.91%) with all vaccine peptides perfectly conserved across 4,690 geographically sampled SARS-CoV-2 genomes. Our proposed MHC class II vaccine formulations provide 97.21% predicted coverage with at least five vaccine peptide-HLA average hits per person with all peptides having an observed mutation probability of ≤ 0.001. We provide an open-source implementation of our design methods (OptiVax), vaccine evaluation tool (EvalVax), as well as the data used in our design efforts here: https://github.com/gifford-lab/optivax.

Evaluating Use Cases for Human Challenge Trials in Accelerating SARS-CoV-2 Vaccine Development.

Human challenge trials (HCTs) have been proposed as a means to accelerate SARS-CoV-2 vaccine development. We identify and discuss 3 potential use cases of HCTs in the current pandemic: evaluating efficacy, converging on correlates of protection, and improving understanding of pathogenesis and the human immune response. We outline the limitations of HCTs and find that HCTs are likely to be most useful for vaccine candidates currently in preclinical stages of development. We conclude that, while currently limited in their application, there are scenarios in which HCTs would be extremely beneficial. Therefore, the option of conducting HCTs to accelerate SARS-CoV-2 vaccine development should be preserved. As HCTs require many months of preparation, we recommend an immediate effort to (1) establish guidelines for HCTs for COVID-19; (2) take the first steps toward HCTs, including preparing challenge virus and making preliminary logistical arrangements; and (3) commit to periodically re-evaluating the utility of HCTs.