Timing HIV infection with a simple and accurate population viral dynamics model |
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Authors: | Daniel B Reeves Morgane Rolland Bethany L Dearlove Yifan Li Merlin L Robb Joshua T Schiffer Peter Gilbert E Fabian Cardozo-Ojeda Bryan T Mayer |
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Affiliation: | 1.Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA;2.US Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, USA;3.Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA;4.Department of Medicine, University of Washington, Seattle, WA, USA;5.Department of Statistics, University of Washington, Seattle, WA, USA |
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Abstract: | Clinical trials for HIV prevention can require knowledge of infection times to subsequently determine protective drug levels. Yet, infection timing is difficult when study visits are sparse. Using population nonlinear mixed-effects (pNLME) statistical inference and viral loads from 46 RV217 study participants, we developed a relatively simple HIV primary infection model that achieved an excellent fit to all data. We also discovered that Aptima assay values from the study strongly correlated with viral loads, enabling imputation of very early viral loads for 28/46 participants. Estimated times between infecting exposures and first positives were generally longer than prior estimates (average of two weeks) and were robust to missing viral upslope data. On simulated data, we found that tighter sampling before diagnosis improved estimation more than tighter sampling after diagnosis. Sampling weekly before and monthly after diagnosis was a pragmatic design for good timing accuracy. Our pNLME timing approach is widely applicable to other infections with existing mathematical models. The present model could be used to simulate future HIV trials and may help estimate protective thresholds from the recently completed antibody-mediated prevention trials. |
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Keywords: | HIV viral dynamics mathematical modelling infection timing clinical trials |
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