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Adults on pre-exposure prophylaxis (tenofovir-emtricitabine) have faster clearance of anti-HIV monoclonal antibody VRC01

Author

Listed:
  • Yunda Huang

    (Fred Hutchinson Cancer Center
    University of Washington)

  • Lily Zhang

    (Fred Hutchinson Cancer Center)

  • Shelly Karuna

    (Fred Hutchinson Cancer Center)

  • Philip Andrew

    (Family Health International)

  • Michal Juraska

    (Fred Hutchinson Cancer Center)

  • Joshua A. Weiner

    (Dartmouth College)

  • Heather Angier

    (Fred Hutchinson Cancer Center)

  • Evgenii Morgan

    (Fred Hutchinson Cancer Center)

  • Yasmin Azzam

    (Fred Hutchinson Cancer Center)

  • Edith Swann

    (National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH))

  • Srilatha Edupuganti

    (Emory University School of Medicine)

  • Nyaradzo M. Mgodi

    (University of Zimbabwe Clinical Trials Research Centre)

  • Margaret E. Ackerman

    (Dartmouth College)

  • Deborah Donnell

    (Fred Hutchinson Cancer Center)

  • Lucio Gama

    (National Institute of Allergy and Infectious Diseases, National Institutes of Health)

  • Peter L. Anderson

    (University of Colorado-AMC)

  • Richard A. Koup

    (National Institute of Allergy and Infectious Diseases, National Institutes of Health)

  • John Hural

    (Fred Hutchinson Cancer Center)

  • Myron S. Cohen

    (University of North Carolina at Chapel Hill)

  • Lawrence Corey

    (Fred Hutchinson Cancer Center
    University of Washington)

  • M. Juliana McElrath

    (Fred Hutchinson Cancer Center
    University of Washington
    University of Washington)

  • Peter B. Gilbert

    (Fred Hutchinson Cancer Center
    University of Washington)

  • Maria P. Lemos

    (Fred Hutchinson Cancer Center)

Abstract

Broadly neutralizing monoclonal antibodies (mAbs) are being developed for HIV-1 prevention. Hence, these mAbs and licensed oral pre-exposure prophylaxis (PrEP) (tenofovir-emtricitabine) can be concomitantly administered in clinical trials. In 48 US participants (men and transgender persons who have sex with men) who received the HIV-1 mAb VRC01 and remained HIV-free in an antibody-mediated-prevention trial (ClinicalTrials.gov #NCT02716675), we conduct a post-hoc analysis and find that VRC01 clearance is 0.08 L/day faster (p = 0.005), and dose-normalized area-under-the-curve of VRC01 serum concentration over-time is 0.29 day/mL lower (p

Suggested Citation

  • Yunda Huang & Lily Zhang & Shelly Karuna & Philip Andrew & Michal Juraska & Joshua A. Weiner & Heather Angier & Evgenii Morgan & Yasmin Azzam & Edith Swann & Srilatha Edupuganti & Nyaradzo M. Mgodi & , 2023. "Adults on pre-exposure prophylaxis (tenofovir-emtricitabine) have faster clearance of anti-HIV monoclonal antibody VRC01," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43399-5
    DOI: 10.1038/s41467-023-43399-5
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    References listed on IDEAS

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    1. D Benkeser & M Carone & M J Van Der Laan & P B Gilbert, 2017. "Doubly robust nonparametric inference on the average treatment effect," Biometrika, Biometrika Trust, vol. 104(4), pages 863-880.
    2. Sung-Youl Ko & Amarendra Pegu & Rebecca S. Rudicell & Zhi-yong Yang & M. Gordon Joyce & Xuejun Chen & Keyun Wang & Saran Bao & Thomas D. Kraemer & Timo Rath & Ming Zeng & Stephen D. Schmidt & John-Pau, 2014. "Enhanced neonatal Fc receptor function improves protection against primate SHIV infection," Nature, Nature, vol. 514(7524), pages 642-645, October.
    3. Shutaro Ishimura & Masato Furuhashi & Yuki Watanabe & Kyoko Hoshina & Takahiro Fuseya & Tomohiro Mita & Yusuke Okazaki & Masayuki Koyama & Marenao Tanaka & Hiroshi Akasaka & Hirofumi Ohnishi & Hideaki, 2013. "Circulating Levels of Fatty Acid-Binding Protein Family and Metabolic Phenotype in the General Population," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-7, November.
    4. van der Laan Mark J. & Rubin Daniel, 2006. "Targeted Maximum Likelihood Learning," The International Journal of Biostatistics, De Gruyter, vol. 2(1), pages 1-40, December.
    5. Laura M. Walker & Michael Huber & Katie J. Doores & Emilia Falkowska & Robert Pejchal & Jean-Philippe Julien & Sheng-Kai Wang & Alejandra Ramos & Po-Ying Chan-Hui & Matthew Moyle & Jennifer L. Mitcham, 2011. "Broad neutralization coverage of HIV by multiple highly potent antibodies," Nature, Nature, vol. 477(7365), pages 466-470, September.
    6. Gruber, Susan & Laan, Mark van der, 2012. "tmle: An R Package for Targeted Maximum Likelihood Estimation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i13).
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