Bayesian estimation of HIV acquisition dates for prevention trials

Raabya Rossenkhan¹, Elena E Giorgi¹, Danica Shao¹, James Ludwig¹, Phillip Labuschagne^{1

2}, Craig A Magaret¹, Thumbi Ndung'u^{3

4

5

6}, Daniel Muema^{3

4}, Kamini Gounder^{3

4}, Krista L Dong⁶, Bruce D Walker^{3

6

7}, Morgane Rolland^{8

9}, Merlin L Robb⁹, Leigh Anne Eller^{8

9}, Fredrick Sawe^{10

11}, Sorachai Nitayaphan¹², Eduard Grebe^{13

14

15}, Michael P Busch^{14

15}, Kevin P Delaney¹⁶, Shelley Facente^{15

17

18}, Lindsay N Carpp¹, Allan C deCamp¹, Yunda Huang^{1

19}, Bette Korber^{20

21}, Michal Juraska¹, Erika Rudnicki¹, Ewelina Kosmider¹, Daniel B Reeves^{1

19}, Bryan T Mayer¹, John Hural¹, Wenjie Deng²², Dylan H Westfall²², Anna Yssel²³, David Matten²³, Tanmoy Bhattacharya^{20

24}, Lawrence Corey^{1

25

26

27}, Peter B Gilbert^{1

25

28}, Carolyn Williamson^{23

29}, James I Mullins^{19

22

26}, Paul T Edlefsen¹

Affiliations

¹ Fred Hutchinson Cancer Center, Vaccine and Infectious Disease Division, Seattle, Washington, USA.
² South African National Bioinformatics Institute, University of the Western Cape, South African Medical Research Council Bioinformatics Unit, Cape Town, South Africa.
³ The Doris Duke Medical Research Institute, University of KwaZulu-Natal, HIV Pathogenesis Programme, Durban, South Africa.
⁴ Africa Health Research Institute, Durban, South Africa.
⁵ Division of Infection and Immunity, University College London, London, United Kingdom.
⁶ Ragon Institute of Massachusetts General, Massachusetts Institute of Technology, and Harvard University, Cambridge, Massachusetts, USA.
⁷ Howard Hughes Medical Institute, Chevy Chase, Maryland, USA.
⁸ Walter Reed Army Institute of Research, U.S. Military HIV Research Program, Silver Spring, Maryland, USA.
⁹ Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA.
¹⁰ Henry M. Jackson Foundation Medical Research International, Nairobi, Kenya.
¹¹ Kenya Medical Research Institute, U.S. Army Medical Research Directorate, Nairobi, Kenya.
¹² Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand.
¹³ Stellenbosch University, South African Centre for Epidemiological Modeling and Analysis (SACEMA), Stellenbosch, South Africa.
¹⁴ University of California at San Francisco, Laboratory Medicine, San Francisco, USA.
¹⁵ Vitalant Research Institute, San Francisco, California, USA.
¹⁶ National Center for HIV Viral Hepatitis, STD and TB Prevention, Division of HIV Prevention, Atlanta, Georgia, USA.
¹⁷ Facente Consulting, Richmond, Virginia, USA.
¹⁸ University of California, Berkeley, School of Public Health, Berkeley, California, USA.
¹⁹ Department of Global Health, University of Washington, Seattle, Washington, USA.
²⁰ New Mexico Consortium, Los Alamos, New Mexico, USA.
²¹ Los Alamos National Laboratory, Theoretical Division, Los Alamos, New Mexico, USA.
²² Department of Microbiology, University of Washington, Seattle, Washington, USA.
²³ Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa.
²⁴ Santa Fe Institute, Santa Fe, New Mexico, USA.
²⁵ Fred Hutchinson Cancer Center, Public Health Sciences Division, Seattle, Washington, USA.
²⁶ Department of Medicine, University of Washington, Seattle, Washington, USA.
²⁷ Department of Laboratory Medicine, University of Washington, Seattle, Washington, USA.
²⁸ Department of Biostatistics, University of Washington, Seattle, Washington, USA.
²⁹ Division of Medical Virology, Wellcome Centre for Infectious Diseases Research in Africa, Department of Pathology Faculty of Health Sciences and National Health Laboratory Service, University of Cape Town, Cape Town, South Africa.

PMID: 40923785
DOI: 10.1128/mbio.01881-25

Bayesian estimation of HIV acquisition dates for prevention trials

Raabya Rossenkhan et al. mBio. 2025.

. 2025 Sep 9:e0188125.

doi: 10.1128/mbio.01881-25. Online ahead of print.

Authors

Affiliations

¹ Fred Hutchinson Cancer Center, Vaccine and Infectious Disease Division, Seattle, Washington, USA.
² South African National Bioinformatics Institute, University of the Western Cape, South African Medical Research Council Bioinformatics Unit, Cape Town, South Africa.
³ The Doris Duke Medical Research Institute, University of KwaZulu-Natal, HIV Pathogenesis Programme, Durban, South Africa.
⁴ Africa Health Research Institute, Durban, South Africa.
⁵ Division of Infection and Immunity, University College London, London, United Kingdom.
⁶ Ragon Institute of Massachusetts General, Massachusetts Institute of Technology, and Harvard University, Cambridge, Massachusetts, USA.
⁷ Howard Hughes Medical Institute, Chevy Chase, Maryland, USA.
⁸ Walter Reed Army Institute of Research, U.S. Military HIV Research Program, Silver Spring, Maryland, USA.
⁹ Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA.
¹⁰ Henry M. Jackson Foundation Medical Research International, Nairobi, Kenya.
¹¹ Kenya Medical Research Institute, U.S. Army Medical Research Directorate, Nairobi, Kenya.
¹² Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand.
¹³ Stellenbosch University, South African Centre for Epidemiological Modeling and Analysis (SACEMA), Stellenbosch, South Africa.
¹⁴ University of California at San Francisco, Laboratory Medicine, San Francisco, USA.
¹⁵ Vitalant Research Institute, San Francisco, California, USA.
¹⁶ National Center for HIV Viral Hepatitis, STD and TB Prevention, Division of HIV Prevention, Atlanta, Georgia, USA.
¹⁷ Facente Consulting, Richmond, Virginia, USA.
¹⁸ University of California, Berkeley, School of Public Health, Berkeley, California, USA.
¹⁹ Department of Global Health, University of Washington, Seattle, Washington, USA.
²⁰ New Mexico Consortium, Los Alamos, New Mexico, USA.
²¹ Los Alamos National Laboratory, Theoretical Division, Los Alamos, New Mexico, USA.
²² Department of Microbiology, University of Washington, Seattle, Washington, USA.
²³ Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa.
²⁴ Santa Fe Institute, Santa Fe, New Mexico, USA.
²⁵ Fred Hutchinson Cancer Center, Public Health Sciences Division, Seattle, Washington, USA.
²⁶ Department of Medicine, University of Washington, Seattle, Washington, USA.
²⁷ Department of Laboratory Medicine, University of Washington, Seattle, Washington, USA.
²⁸ Department of Biostatistics, University of Washington, Seattle, Washington, USA.
²⁹ Division of Medical Virology, Wellcome Centre for Infectious Diseases Research in Africa, Department of Pathology Faculty of Health Sciences and National Health Laboratory Service, University of Cape Town, Cape Town, South Africa.

PMID: 40923785
DOI: 10.1128/mbio.01881-25

Abstract

Accurate timing estimates of when participants acquire HIV in HIV prevention trials are necessary for determining antibody levels at acquisition. The Antibody-Mediated Prevention (AMP) Studies showed that a passively administered broadly neutralizing antibody can prevent the acquisition of HIV from a neutralization-sensitive virus. We developed a pipeline for estimating the date of detectable HIV acquisition (DDA) in AMP Study participants using diagnostic and viral sequence data. Using a Bayesian strategy that combines three streams of data (REN [rev/vpu/env/Δnef] sequence, GP [gag/Δpol] sequence, and diagnostic) where their 95% credible intervals overlap based on pre-specified criteria and decision rules. We evaluated the performance of our AMP pipeline using PacBio viral sequence data from 41 participants across two prospective acute HIV acquisition cohort studies, FRESH and RV217, with twice-weekly sampling. These cohort studies enrolled young women in South Africa and men and women in Kenya and Thailand, respectively, with a high likelihood of HIV acquisition. In evaluating performance, "true DDA" was the center of bounds between last-negative and first-positive RNA diagnostic tests (median time 4 days, range 2-7 days); bias was the mean difference between estimated and true DDA. Using diagnostic data alone yielded timing estimates with a bias of 2.4 days and root mean square error (RMSE) of 7.9 days. These results were improved using sequence + diagnostic data (bias 1.5 days, RMSE 6.9 days), as well as by restricting sequence-based estimation to samples from ≤5 weeks post-DDA (bias 0.2 days, RMSE 7.8 days).IMPORTANCEIn HIV prevention trials, accurate timing estimates of when individual participants acquire HIV can be used to estimate antibody levels at the time of acquisition, which is useful for projecting antibody levels needed for prevention. The results we report here suggest that if sequence-based estimation of acquisition timing is used in future clinical trials of combination broadly neutralizing antibody (bnAb) regimens or multispecific bnAbs for HIV prevention, a sampling frequency of at least monthly is needed. Moreover, in the samples analyzed here, we observed less bias in sequence-based timing estimation for samples taken <5 weeks post-DDA. This observation is consistent with the timing of immune-driven selective pressures that may negatively impact the power to detect acquisition sieve effects.

Keywords: Antibody-Mediated Prevention (AMP) Studies; Bayesian posterior distribution; FRESH; HIV; RV217; acute acquisition cohort; date of detectable acquisition.

PubMed Disclaimer

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Bayesian estimation of HIV acquisition dates for prevention trials

Affiliations

Bayesian estimation of HIV acquisition dates for prevention trials

Authors

Affiliations

Abstract

Grants and funding