Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research
Summary: 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 Predi...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004223016723 |
| _version_ | 1797739683941187584 |
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| author | Brian D. Williamson Craig A. Magaret Shelly Karuna Lindsay N. Carpp Huub C. Gelderblom Yunda Huang David Benkeser Peter B. Gilbert |
| author_facet | Brian D. Williamson Craig A. Magaret Shelly Karuna Lindsay N. Carpp Huub C. Gelderblom Yunda Huang David Benkeser Peter B. Gilbert |
| author_sort | Brian D. Williamson |
| collection | DOAJ |
| description | Summary: 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. |
| first_indexed | 2024-03-12T14:01:48Z |
| format | Article |
| id | doaj.art-6643c1ff735844fdb145351d0dac803d |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-03-12T14:01:48Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-6643c1ff735844fdb145351d0dac803d2023-08-22T04:06:59ZengElsevieriScience2589-00422023-09-01269107595Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention researchBrian D. Williamson0Craig A. Magaret1Shelly Karuna2Lindsay N. Carpp3Huub C. Gelderblom4Yunda Huang5David Benkeser6Peter B. Gilbert7Biostatistics Division; Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, USA; Vaccine and Infectious Disease Division; Fred Hutchinson Cancer Center, Seattle, WA 98109, USAVaccine and Infectious Disease Division; Fred Hutchinson Cancer Center, Seattle, WA 98109, USAVaccine and Infectious Disease Division; Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; GreenLight Biosciences, Medford, MA 02155, USAVaccine and Infectious Disease Division; Fred Hutchinson Cancer Center, Seattle, WA 98109, USAVaccine and Infectious Disease Division; Fred Hutchinson Cancer Center, Seattle, WA 98109, USAVaccine and Infectious Disease Division; Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; Department of Global Health; University of Washington, Seattle, WA 98105, USADepartment of Biostatistics and Bioinformatics; Emory University, Atlanta, GA 30322, USAVaccine and Infectious Disease Division; Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; Department of Biostatistics; University of Washington, Seattle, WA 98195, USA; Corresponding authorSummary: 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.http://www.sciencedirect.com/science/article/pii/S2589004223016723ImmunologyImmunological methodsVirologyMathematical biosciences |
| spellingShingle | Brian D. Williamson Craig A. Magaret Shelly Karuna Lindsay N. Carpp Huub C. Gelderblom Yunda Huang David Benkeser Peter B. Gilbert Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research iScience Immunology Immunological methods Virology Mathematical biosciences |
| title | Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research |
| title_full | Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research |
| title_fullStr | Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research |
| title_full_unstemmed | Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research |
| title_short | Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research |
| title_sort | application of the slapnap statistical learning tool to broadly neutralizing antibody hiv prevention research |
| topic | Immunology Immunological methods Virology Mathematical biosciences |
| url | http://www.sciencedirect.com/science/article/pii/S2589004223016723 |
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