Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research

Brian D Williamson; Craig A Magaret; Shelly Karuna; Lindsay N Carpp; Huub C Gelderblom; Yunda Huang; David Benkeser; Peter B Gilbert

doi:10.1016/j.isci.2023.107595

Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research

iScience. 2023 Aug 9;26(9):107595. doi: 10.1016/j.isci.2023.107595. eCollection 2023 Sep 15.

Authors

Brian D Williamson^{1

2}, Craig A Magaret², Shelly Karuna^{2

3}, Lindsay N Carpp², Huub C Gelderblom², Yunda Huang^{2

4}, David Benkeser⁵, Peter B Gilbert^{2

6

7}

Affiliations

¹ Biostatistics Division; Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, USA.
² Vaccine and Infectious Disease Division; Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
³ GreenLight Biosciences, Medford, MA 02155, USA.
⁴ Department of Global Health; University of Washington, Seattle, WA 98105, USA.
⁵ Department of Biostatistics and Bioinformatics; Emory University, Atlanta, GA 30322, USA.
⁶ Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
⁷ Department of Biostatistics; University of Washington, Seattle, WA 98195, USA.

Abstract

Combination monoclonal broadly neutralizing antibody (bnAb) regimens are in clinical development for HIV prevention, necessitating additional knowledge of bnAb neutralization potency/breadth against circulating viruses. Williamson et al. (2021) described a software tool, Super LeArner Prediction of NAb Panels (SLAPNAP), with application to any HIV bnAb regimen with sufficient neutralization data against a set of viruses in the Los Alamos National Laboratory's Compile, Neutralize, and Tally Nab Panels repository. SLAPNAP produces a proteomic antibody resistance (PAR) score for Env sequences based on predicted neutralization resistance and estimates variable importance of Env amino acid features. We apply SLAPNAP to compare HIV bnAb regimens undergoing clinical testing, finding improved power for downstream sieve analyses and increased precision for comparing neutralization potency/breadth of bnAb regimens due to the inclusion of PAR scores of Env sequences with much larger sample sizes available than for neutralization outcomes. SLAPNAP substantially improves bnAb regimen characterization, ranking, and down-selection.

Keywords: Immunological methods; Immunology; Mathematical biosciences; Virology.

Abstract

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