Browsing by Author "Pilcher, Christopher D."

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    Computational analysis of antibody dynamics identifies recent HIV-1 infection
    (American Society for Clinical Investigation, 2018) Seaton, Kelly E.; Vandergrift, Nathan A.; Deal, Aaron W.; Rountree, Wes; Bainbridge, John; Grebe, Eduard; Anderson, David A.; Sawant, Sheetal; Shen, Xiaoying; Yates, Nicole L.; Denny, Thomas N.; Liao, Hua-Xin; Haynes, Barton F.; Robb, Merlin L.; Parkin, Neil; Santos, Breno R.; Garrett, Nigel; Price, Matthew A.; Naniche, Denise; Duerr, Ann C.; The CEPHIA group; Keating, Sheila; Hampton, Dylan; Facente, Shelley; Marson, Kara; Welte, Alex; Pilcher, Christopher D.; Cohen, Myron S.; Tomaras, Georgia D.
    Accurate HIV-1 incidence estimation is critical to the success of HIV-1 prevention strategies. Current assays are limited by high false recent rates (FRRs) in certain populations and a short mean duration of recent infection (MDRI). Dynamic early HIV-1 antibody response kinetics were harnessed to identify biomarkers for improved incidence assays. We conducted retrospective analyses on circulating antibodies from known recent and longstanding infections and evaluated binding and avidity measurements of Env and non-Env antigens and multiple antibody forms (i.e., IgG, IgA, IgG3, IgG4, dIgA, and IgM) in a diverse panel of 164 HIV-1–infected participants (clades A, B, C). Discriminant function analysis identified an optimal set of measurements that were subsequently evaluated in a 324-specimen blinded biomarker validation panel. These biomarkers included clade C gp140 IgG3, transmitted/founder clade C gp140 IgG4 avidity, clade B gp140 IgG4 avidity, and gp41 immunodominant region IgG avidity. MDRI was estimated at 215 day or alternatively, 267 days. FRRs in untreated and treated subjects were 5.0% and 3.6%, respectively. Thus, computational analysis of dynamic HIV-1 antibody isotype and antigen interactions during infection enabled design of a promising HIV-1 recency assay for improved cross-sectional incidence estimation.
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    Interpreting HIV diagnostic histories into infection time estimates : analytical framework and online tool
    (BMC (part of Springer Nature), 2019-10-26) Grebe, Eduard; Facente, Shelley N.; Bingham, Jeremy; Pilcher, Christopher D.; Powrie, Andrew; Gerber, Jarryd; Priede, Gareth; Chibawara, Trust; Busch, Michael P.; Murphy, Gary; Kassanjee, Reshma; Welte, Alex
    Background: It is frequently of epidemiological and/or clinical interest to estimate the date of HIV infection or time-since-infection of individuals. Yet, for over 15 years, the only widely-referenced infection dating algorithm that utilises diagnostic testing data to estimate time-since-infection has been the ‘Fiebig staging’ system. This defines a number of stages of early HIV infection through various standard combinations of contemporaneous discordant diagnostic results using tests of different sensitivity. To develop a new, more nuanced infection dating algorithm, we generalised the Fiebig approach to accommodate positive and negative diagnostic results generated on the same or different dates, and arbitrary current or future tests – as long as the test sensitivity is known. For this purpose, test sensitivity is the probability of a positive result as a function of time since infection. Methods: The present work outlines the analytical framework for infection date estimation using subject-level diagnostic testing histories, and data on test sensitivity. We introduce a publicly-available online HIV infection dating tool that implements this estimation method, bringing together 1) curatorship of HIV test performance data, and 2) infection date estimation functionality, to calculate plausible intervals within which infection likely became detectable for each individual. The midpoints of these intervals are interpreted as infection time ‘point estimates’ and referred to as Estimated Dates of Detectable Infection (EDDIs). The tool is designed for easy bulk processing of information (as may be appropriate for research studies) but can also be used for individual patients (such as in clinical practice). Results: In many settings, including most research studies, detailed diagnostic testing data are routinely recorded, and can provide reasonably precise estimates of the timing of HIV infection. We present a simple logic to the interpretation of diagnostic testing histories into infection time estimates, either as a point estimate (EDDI) or an interval (earliest plausible to latest plausible dates of detectable infection), along with a publicly-accessible online tool that supports wide application of this logic. Conclusions: This tool, available at https://tools.incidence-estimation.org/idt/, is readily updatable as test technology evolves, given the simple architecture of the system and its nature as an open source project.
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    Performance comparison of the Maxim and Sedia Limiting Antigen Avidity assays for HIV incidence surveillance
    (Public Library of Science, 2019-07-26) Sempa, Joseph B.; Welte, Alex; Busch, Michael P.; Hall, Jake; Hampton, Dylan; Facente, Shelley N.; Keating, Sheila M.; Marson, Kara; Parkin, Neil; Pilcher, Christopher D.; Murphy, Gary; Grebe, Eduard
    Background: Two manufacturers, Maxim Biomedical and Sedia Biosciences Corporation, supply CDC-approved versions of the HIV-1 Limiting Antigen Avidity EIA (LAg) for detecting ‘recent’ HIV infection in cross-sectional incidence estimation. This study assesses and compares the performance of the two assays for incidence surveillance. Methods: We ran both assays on a panel of 2,500 well-characterized HIV-1-infected specimens. We analysed concordance of assay results, assessed reproducibility using repeat testing and estimated mean durations of recent infection (MDRIs) and false-recent rates (FRRs) for a range of normalized optical density (ODn) thresholds, alone and in combination with viral load thresholds. We defined three hypothetical surveillance scenarios, similar to the Kenyan and South African epidemics, and a concentrated epidemic. These scenarios allowed us to evaluate the precision of incidence estimates obtained by means of various recent infection testing algorithms (RITAs) based on each of the two assays. Results: The Maxim assay produced lower ODn values than the Sedia assay on average, largely as a result of higher calibrator readings (mean OD of 0.749 vs. 0.643), with correlation of normalized readings lower (R2 = 0.908 vs. R2 = 0.938). Reproducibility on blinded control specimens was slightly better for Maxim. The MDRI of a Maxim-based algorithm at the ‘standard’ threshold (ODn ≤1.5 & VL >1,000) was 201 days (95% CI: 180,223) and for Sedia 171 (152,191). The difference Differences in MDRI were estimated at 32.7 (22.9,42.8) and 30.9 days (21.7,40.7) for the two algorithms, respectively. Commensurately, the Maxim algorithm had a higher FRR in treatment-naive subjects (1.7% vs. 1.1%). The two assays produced similar precision of incidence estimates in the three surveillance scenarios. Conclusions: Differences between the assays can be primarily attributed to the calibrators supplied by the manufacturers. Performance for surveillance was extremely similar, although different thresholds were optimal (i.e. produced the lowest variance of incidence estimates) and at any given ODn threshold, different estimates of MDRI and FRR were obtained. The two assays cannot be treated as interchangeable: assay and algorithm-specific performance characteristic estimates must be used for survey planning and incidence estimation.