Super LeArner Prediction of NAb Panels (SLAPNAP): A Containerized Tool for Predicting Combination Monoclonal Broadly Neutralizing Antibody Sensitivity

Summary Single broadly neutralizing antibody (bnAb) regimens are currently being evaluated in randomized trials for prevention efficacy against HIV-1 infection. Subsequent trials will evaluate combination bnAb regimens (e.g., cocktails, multi-specific antibodies), which demonstrate higher potency and breadth in vitro compared to single bnAbs. Given the large number of potential regimens in the research pipeline, methods for down-selecting these regimens into efficacy trials are of great interest. To aid the down-selection process, we developed Super LeArner Prediction of NAb Panels (SLAPNAP), a software tool for training and evaluating machine learning models that predict in vitro neutralization resistance of HIV Envelope pseudoviruses to a given single or combination bnAb regimen, based on Envelope amino acid sequence features. SLAPNAP also provides measures of variable importance of sequence features. These results can rank bnAb regimens by their potential prevention efficacy and aid assessments of how prevention efficacy depends on sequence features. Availability and Implementation SLAPNAP is a freely available docker image that can be downloaded from DockerHub (https://hub.docker.com/r/slapnap/slapnap). Source code and documentation are available at GitHub (respectively, https://github.com/benkeser/slapnap and https://benkeser.github.io/slapnap/). Contact David Benkeser, benkeser@emory.edu

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