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

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

doi:10.1016/j.isci.2023.107595

Cited by 1 publication

(3 citation statements)

References 59 publications

(105 reference statements)

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“…We consider several bnAb combination regimens with data available in CATNAP. These regimens consist of those undergoing HIV Vaccine Trials Network (HVTN) or HIV Prevention Trials Network (HPTN) clinical testing as of October 2022 (Williamson et al, 2023) and those with at least 125 pseudoviruses with direct measurement of neutralization by the bnAb combination (Williamson et al, 2021). The full list of bnAb combination regimens is provided in Table 1.…”

Section: Methodsmentioning

confidence: 99%

“…Other outcomes, including multiple susceptibility – defined as the binary indcator that k bnAbs in the regimen have IC 80 < 1 µg/mL – could be used instead. These prediction models may be used to compare bnAb regimens when determining which to pursue in further research (Dănăilă and Buiu, 2022; Williamson et al, 2023), since neutralization is associated with prevention efficacy (Corey et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…However, prediction performance for some individual bnAbs has exceeded that for the combination (Williamson et al, 2023). This suggests a second possible method for predicting combination neutralization outcomes: combining the individual-bnAb predicted values and using these combinations to predict neutralization by the combination regimen.…”

Section: Introductionmentioning

confidence: 99%

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Predicting neutralization susceptibility to combination HIV-1 monoclonal broadly neutralizing antibody regimens

Williamson,

Wu,

Huang

et al. 2023

Preprint

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Combination monoclonal broadly neutralizing antibodies (bnAbs) are currently being developed for preventing HIV-1 infection. Recent work has focused on predicting in vitro neutralization potency of both individual bnAbs and combination regimens against HIV-1 pseudoviruses using Env sequence features. To predict in vitro combination regimen neutralization potency against a given HIV-1 pseudovirus, previous approaches have applied mathematical models to combine individual-bnAb neutralization and have predicted this combined neutralization value; we call this the combine-then-predict (CP) approach. However, prediction performance for some individual bnAbs has exceeded that for the combination, leading to another possibility: combining the individual-bnAb predicted values and using these to predict combination regimen neutralization; we call this the predict-then-combine (PC) approach. We explore both approaches in both simulated data and data from the Los Alamos National Laboratory’s Compile, Neutralize, and Tally NAb Panels repository. The CP approach is superior to the PC approach when the neutralization outcome of interest is binary (e.g., neutralization susceptibility, defined as inhibitory concentration<1 µg/mL. For continuous outcomes, the CP approach performs at least as well as the PC approach, and is superior to the PC approach when the individual-bnAb prediction algorithms have poor performance. This knowledge may be used when building prediction models for novel antibody combinations in the absence of in vitro neutralization data for the antibody combination; this, in turn, will aid in the evaluation and down-selection of these antibody combinations into prevention efficacy trials.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting neutralization susceptibility to combination HIV-1 monoclonal broadly neutralizing antibody regimens

Williamson,

Wu,

Huang

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

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

Cited by 1 publication

References 59 publications

Predicting neutralization susceptibility to combination HIV-1 monoclonal broadly neutralizing antibody regimens

Predicting neutralization susceptibility to combination HIV-1 monoclonal broadly neutralizing antibody regimens

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