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Can incorporating genotyping data into efficacy estimators improve efficiency of early phase malaria vaccine trials?

  1. Author:
    Potter, Gail E
    Callier,Viviane
    Shrestha, Biraj
    Joshi, Sudhaunshu
    Dwivedi, Ankit
    Silva, Joana C
    Laurens, Matthew B
    Follmann, Dean A
    Deye, Gregory A
  2. Author Address

    Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA. gail.potter@nih.gov., Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA., Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, USA., Institute for Genomic Sciences, University of Maryland School of Medicine, Baltimore, MD, USA., Department of Microbiology & Immunology, University of Maryland School of Medicine, Baltimore, MD, USA., Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA., AstraZeneca PLC, Gaithersburg, MD, USA.,
    1. Year: 2023
    2. Date: Dec 19
    3. Epub Date: 2023 12 19
  1. Journal: Malaria Journal
    1. 22
    2. 1
    3. Pages: 383
  2. Type of Article: Article
  3. Article Number: 383
  1. Abstract:

    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.

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External Sources

  1. DOI: 10.1186/s12936-023-04802-0
  2. PMID: 38115002
  3. PMCID: PMC10729369
  4. PII : 10.1186/s12936-023-04802-0

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  1. Fiscal Year: FY2023-2024
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