BACKGROUND: Early phase malaria vaccine field trials typically measure malaria infection by PCR or thick blood smear microscopy performed on serially sampled blood. Vaccine efficacy (VE) is the proportion reduction in an endpoint due to vaccination and is often calculated as VEHR = 1-hazard ratio or VERR = 1-risk ratio. Genotyping information can distinguish different clones and distinguish multiple infections over time, potentially increasing statistical power. This paper investigates two alternative VE endpoints incorporating genotyping information: VEmolFOI, the vaccine-induced proportion reduction in incidence of new clones acquired over time, and VEC, the vaccine-induced proportion reduction in mean number of infecting clones per exposure.
METHODS: Power of VEmolFOI and VEC was compared to that of VEHR and VERR by simulations and analytic derivations, and the four VE methods were applied to three data sets: a Phase 3 trial of RTS,S malaria vaccine in 6912 African infants, a Phase 2 trial of PfSPZ Vaccine in 80 Burkina Faso adults, and a trial comparing Plasmodium vivax incidence in 466 Papua New Guinean children after receiving chloroquine + artemether lumefantrine with or without primaquine (as these VE methods can also quantify effects of other prevention measures). By destroying hibernating liver-stage P. vivax, primaquine reduces subsequent reactivations after treatment completion.
RESULTS: In the trial of RTS,S vaccine, a significantly reduced number of clones at first infection was observed, but this was not the case in trials of PfSPZ Vaccine or primaquine, although the PfSPZ trial lacked power to show a reduction. Resampling smaller data sets from the large RTS,S trial to simulate phase 2 trials showed modest power gains from VEC compared to VEHR for data like those from RTS,S, but VEC is less powerful than VEHR for trials in which the number of clones at first infection is not reduced. VEmolFOI was most powerful in model-based simulations, but only the primaquine trial collected enough serial samples to precisely estimate VEmolFOI. The primaquine VEmolFOI estimate decreased after most control arm liver-stage infections reactivated (which mathematically resembles a waning vaccine), preventing VEmolFOI from improving power.
CONCLUSIONS: The power gain from the genotyping methods depends on the context. Because input parameters for early phase power calculations are often uncertain, these estimators are not recommended as primary endpoints for small trials unless supported by targeted data analysis.
BACKGROUND: NCT00866619, NCT02663700, NCT02143934.

UNASSIGNED: Early phase malaria vaccine field trials typically measure malaria infection by PCR or thick blood smear microscopy performed on serially sampled blood. Vaccine efficacy (VE) is the proportion reduction in an endpoint due to vaccination and is often calculated as VEHR=1 - hazard ratio or VERR=1 - risk ratio. Genotyping information can distinguish different clones and distinguish multiple infections over time, potentially increasing statistical power. This paper investigates two alternative VE endpoints incorporating genotyping information: VEmolFOI, the vaccine-induced proportion reduction in incidence of new clones acquired over time, and VEC, the vaccine-induced proportion reduction in mean number of infecting clones per exposure.
UNASSIGNED: We used simulations and analytic derivations to compare power of these methods to VEHR and VERR and applied them to three data sets: a Phase 3 trial of RTS,S malaria vaccine in 6912 African infants, a Phase 2 trial of PfSPZ Vaccine in 80 Burkina Faso adults, and a trial comparing Plasmodium vivax incidence in 466 Papua New Guinean children after receiving chloroquine + artemether lumefantrine with or without primaquine (as these VE methods can also quantify effects of other prevention measures). By destroying hibernating liver-stage P. vivax, primaquine reduces subsequent reactivations after treatment completion.
UNASSIGNED: The RTS,S vaccine significantly reduced the number of clones at first infection, but PfSPZ vaccine and primaquine did not. Resampling smaller data sets from the large RTS,S trial to simulate phase 2 trials showed modest power gains from VEC compared to VEHR for data like RTS,S, but VEC is less powerful than VEHR for vaccines which do not reduce the number of clones at first infection. VEmolFOI was most powerful in model-based simulations, but only the primaquine trial collected enough serial samples to precisely estimate VEmolFOI. The primaquine VEmolFOI estimate decreased after most control arm liver-stage infections reactivated (which mathematically resembles a waning vaccine), preventing VEmolFOI from improving power.
UNASSIGNED: The power gain from the genotyping methods depends on the context. Because input parameters for early phase power calculations are often uncertain, we recommend against these estimators as primary endpoints for small trials unless supported by targeted data analysis.
UNASSIGNED: NCT00866619, NCT02663700, NCT02143934.

Reliable quantification of gene and protein expression has potential to contribute significantly to the characterization of hypothesized modes of action (MOA) or adverse outcome pathways for critical effects of toxicants. Quantitative analysis of gene expression by benchmark dose (BMD) modeling has been facilitated by the development of effective software tools. In contrast, protein expression is still generally quantified by a less robust effect level (no or lowest [adverse] effect levels) approach, which minimizes its potential utility in the consideration of dose-response and temporal concordance for key events in hypothesized MOAs. BMD modeling is applied here to toxicological data on testicular toxicity to investigate its potential utility in analyzing protein expression relevant to the proposed MOA to inform human health risk assessment. The results illustrate how the BMD analysis of protein expression in animal tissues in response to toxicant exposure: (1) complements other toxicity data, and (2) contributes to consideration of the empirical concordance of dose-response relationships, as part of the weight of evidence for hypothesized MOAs to facilitate consideration and application in regulatory risk assessment. Lack of BMD analysis in proteomics has likely limited its use for these purposes. This paper illustrates the added value of BMD modeling to support and strengthen hypothetical MOAs as a basis to facilitate the translation and uptake of the results of proteomic research into risk assessment.