Bayesian estimation of HIV acquisition dates for prevention trials Article Swipe
Raabya Rossenkhan
,
Elena E. Giorgi
,
Danica Shao
,
James P. Ludwig
,
Jan Phillipus Lourens Labuschagne
,
Craig A. Magaret
,
Thumbi Ndung’u
,
Daniel Muema
,
Kamini Gounder
,
Krista L. Dong
,
Bruce D. Walker
,
Morgane Rolland
,
Merlin L. Robb
,
Leigh Anne Eller
,
Fred Sawe
,
Sorachai Nitayaphan
,
Eduard Grebe
,
Michael P. Busch
,
Kevin P. Delaney
,
Shelley N. Facente
,
Lindsay N. Carpp
,
Allan C. deCamp
,
Yunda Huang
,
Bette Korber
,
Michal Juraska
,
Erika Rudnicki
,
Ewelina Kosmider
,
Daniel B. Reeves
,
Bryan T. Mayer
,
John Hural
,
Wenjie Deng
,
Dylan H. Westfall
,
Anna Yssel
,
David Matten
,
Tanmoy Bhattacharya
,
Lawrence Corey
,
Peter B. Gilbert
,
Carolyn Williamson
,
James I. Mullins
,
Paul T. Edlefsen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1128/mbio.01881-25
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1128/mbio.01881-25
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). IMPORTANCE In 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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1128/mbio.01881-25
- OA Status
- gold
- Cited By
- 2
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4414082484
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4414082484Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1128/mbio.01881-25Digital Object Identifier
- Title
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Bayesian estimation of HIV acquisition dates for prevention trialsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-09-09Full publication date if available
- Authors
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Raabya Rossenkhan, Elena E. Giorgi, Danica Shao, James P. Ludwig, Jan Phillipus Lourens Labuschagne, Craig A. Magaret, Thumbi Ndung’u, Daniel Muema, Kamini Gounder, Krista L. Dong, Bruce D. Walker, Morgane Rolland, Merlin L. Robb, Leigh Anne Eller, Fred Sawe, Sorachai Nitayaphan, Eduard Grebe, Michael P. Busch, Kevin P. Delaney, Shelley N. Facente, Lindsay N. Carpp, Allan C. deCamp, Yunda Huang, Bette Korber, Michal Juraska, Erika Rudnicki, Ewelina Kosmider, Daniel B. Reeves, Bryan T. Mayer, John Hural, Wenjie Deng, Dylan H. Westfall, Anna Yssel, David Matten, Tanmoy Bhattacharya, Lawrence Corey, Peter B. Gilbert, Carolyn Williamson, James I. Mullins, Paul T. EdlefsenList of authors in order
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https://doi.org/10.1128/mbio.01881-25Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1128/mbio.01881-25Direct OA link when available
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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49Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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