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Can Incorporating Genotyping Data into Efficacy Estimators Improve Efficiency of Early Phase Malaria Vaccine Trials?

Overview
Journal Malar J
Publisher Biomed Central
Specialty Tropical Medicine
Date 2023 Dec 20
PMID 38115002
Authors
Affiliations
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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 VE = 1-hazard ratio or VE = 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: VE, the vaccine-induced proportion reduction in incidence of new clones acquired over time, and VE, the vaccine-induced proportion reduction in mean number of infecting clones per exposure.

Methods: Power of VE and VE was compared to that of VE and VE 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 VE compared to VE for data like those from RTS,S, but VE is less powerful than VE for trials in which the number of clones at first infection is not reduced. VE was most powerful in model-based simulations, but only the primaquine trial collected enough serial samples to precisely estimate VE. The primaquine VE estimate decreased after most control arm liver-stage infections reactivated (which mathematically resembles a waning vaccine), preventing VE 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.

Trial Registrations: NCT00866619, NCT02663700, NCT02143934.

Citing Articles

Distinguishing new from persistent infections at the strain level using longitudinal genotyping data.

Nickols W, Schwabl P, Niangaly A, Murphy S, Crompton P, Neafsey D bioRxiv. 2025; .

PMID: 39975299 PMC: 11839113. DOI: 10.1101/2025.02.06.636982.

References
1.
Taylor A, Watson J, Chu C, Puaprasert K, Duanguppama J, Day N . Resolving the cause of recurrent Plasmodium vivax malaria probabilistically. Nat Commun. 2019; 10(1):5595. PMC: 6898227. DOI: 10.1038/s41467-019-13412-x. View

2.
Briggs J, Teyssier N, Nankabirwa J, Rek J, Jagannathan P, Arinaitwe E . Sex-based differences in clearance of chronic infection. Elife. 2020; 9. PMC: 7591246. DOI: 10.7554/eLife.59872. View

3.
Sirima S, Ouedraogo A, Tiono A, Kabore J, Bougouma E, Ouattara M . A randomized controlled trial showing safety and efficacy of a whole sporozoite vaccine against endemic malaria. Sci Transl Med. 2022; 14(674):eabj3776. PMC: 10041996. DOI: 10.1126/scitranslmed.abj3776. View

4.
Buyse M, Sargent D, Grothey A, Matheson A, De Gramont A . Biomarkers and surrogate end points--the challenge of statistical validation. Nat Rev Clin Oncol. 2010; 7(6):309-17. DOI: 10.1038/nrclinonc.2010.43. View

5.
Ortega-Villa A, Nason M, Follmann D . The mechanistic analysis of founder virus data in challenge models. Stat Med. 2021; 40(20):4492-4504. DOI: 10.1002/sim.9075. View