IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-43399-5.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-43399-5
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-43399-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. 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).
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kara E. Rudolph & Jonathan Levy & Mark J. van der Laan, 2021. "Transporting stochastic direct and indirect effects to new populations," Biometrics, The International Biometric Society, vol. 77(1), pages 197-211, March.
    2. Susan Gruber & Mark J. van der Laan, 2013. "An Application of Targeted Maximum Likelihood Estimation to the Meta-Analysis of Safety Data," Biometrics, The International Biometric Society, vol. 69(1), pages 254-262, March.
    3. Veronica Sciannameo & Gian Paolo Fadini & Daniele Bottigliengo & Angelo Avogaro & Ileana Baldi & Dario Gregori & Paola Berchialla, 2022. "Assessment of Glucose Lowering Medications’ Effectiveness for Cardiovascular Clinical Risk Management of Real-World Patients with Type 2 Diabetes: Targeted Maximum Likelihood Estimation under Model Mi," IJERPH, MDPI, vol. 19(22), pages 1-13, November.
    4. Ziyun Xu & Éric Archambault, 2015. "Chinese interpreting studies: structural determinants of MA students’ career choices," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(2), pages 1041-1058, November.
    5. Torben Martinussen & Mats Julius Stensrud, 2023. "Estimation of separable direct and indirect effects in continuous time," Biometrics, The International Biometric Society, vol. 79(1), pages 127-139, March.
    6. Sherri Rose & Sharon‐Lise Normand, 2019. "Double robust estimation for multiple unordered treatments and clustered observations: Evaluating drug‐eluting coronary artery stents," Biometrics, The International Biometric Society, vol. 75(1), pages 289-296, March.
    7. David Cheng & Ashwin N. Ananthakrishnan & Tianxi Cai, 2021. "Robust and efficient semi‐supervised estimation of average treatment effects with application to electronic health records data," Biometrics, The International Biometric Society, vol. 77(2), pages 413-423, June.
    8. Iván Díaz & Nima S. Hejazi, 2020. "Causal mediation analysis for stochastic interventions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 661-683, July.
    9. Sun Hao & Ertefaie Ashkan & Lu Xin & Johnson Brent A., 2020. "Improved Doubly Robust Estimation in Marginal Mean Models for Dynamic Regimes," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 300-314, January.
    10. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
    11. Youmi Suk & Jee-Seon Kim & Hyunseung Kang, 2021. "Hybridizing Machine Learning Methods and Finite Mixture Models for Estimating Heterogeneous Treatment Effects in Latent Classes," Journal of Educational and Behavioral Statistics, , vol. 46(3), pages 323-347, June.
    12. Jeremiah Jones & Ashkan Ertefaie & Susan M. Shortreed, 2023. "Rejoinder to “Reader reaction to ‘Outcome‐adaptive Lasso: Variable selection for causal inference’ by Shortreed and Ertefaie (2017)”," Biometrics, The International Biometric Society, vol. 79(1), pages 521-525, March.
    13. Jenny Häggström, 2018. "Data†driven confounder selection via Markov and Bayesian networks," Biometrics, The International Biometric Society, vol. 74(2), pages 389-398, June.
    14. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression: Long Term, Mediated, and Time Varying Treatment Effects," Papers 2112.14249, arXiv.org, revised Mar 2024.
    15. Fangzhu Zhao & Zachary T. Berndsen & Nuria Pedreño-Lopez & Alison Burns & Joel D. Allen & Shawn Barman & Wen-Hsin Lee & Srirupa Chakraborty & Sandrasegaram Gnanakaran & Leigh M. Sewall & Gabriel Ozoro, 2022. "Molecular insights into antibody-mediated protection against the prototypic simian immunodeficiency virus," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    16. Joseph Antonelli & Georgia Papadogeorgou & Francesca Dominici, 2022. "Causal inference in high dimensions: A marriage between Bayesian modeling and good frequentist properties," Biometrics, The International Biometric Society, vol. 78(1), pages 100-114, March.
    17. Xiang Zhou, 2022. "Semiparametric estimation for causal mediation analysis with multiple causally ordered mediators," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 794-821, July.
    18. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    19. S Ariane Christie & Amanda S Conroy & Rachael A Callcut & Alan E Hubbard & Mitchell J Cohen, 2019. "Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-13, April.
    20. Waverly Wei & Maya Petersen & Mark J van der Laan & Zeyu Zheng & Chong Wu & Jingshen Wang, 2023. "Efficient targeted learning of heterogeneous treatment effects for multiple subgroups," Biometrics, The International Biometric Society, vol. 79(3), pages 1934-1946, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43399-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.