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

• Type of Material: Article
• Authors: Brian D. Williamson, Kaiser Permanente Washington Health Research Institute; Criag A. Magaret, Fred Hutchinson Cancer Center; Shelly Karuna, Fred Hutchinson Cancer Center; Lindsay N. Carpp, Fred Hutchinson Cancer Center; Huub C. Gelderblom, Fred Hutchinson Cancer Center; Yunda Huang, Fred Hutchinson Cancer Center; David Benkeser, Emory University; Peter B. Gilbert, Fred Hutchinson Cancer Center
• Language: English
• Date: 2023-09-15
• Publisher: Elseiver
• Publication Version: Final Published Version
• Copyright Statement: © 2023 The Authors
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): https://doi.org/10.1016%2Fj.isci.2023.107595
• Title of Journal or Parent Work: iScience
• Volume: 26
• Issue: 9
• Start Page: 107595
• Grant/Funding Information: This work was supported by the National Institute of Allergy and Infectious Diseases (NIAID, https://www.niaid.nih.gov/) through awards UM1 AI068635 [SDMC: HIV Vaccine Trials Network] to Y.H. and P.B.G. and R37AI054165 to P.B.G. This work was also supported by Scientific Computing Infrastructure at Fred Hutch funded by ORIP grant S10OD028685.
• Keywords: HIV; Super LeArner Prediction of NAb Panels (SLAPNAP); Immunology; Virology; Immunological methods; proteomic antibody resistance (PAR); Mathematical biosciences
• Research Categories: Biology, Virology; Mathematics; Health Sciences, Immunology

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.