Williamson Brian D, Magaret Craig A, Karuna Shelly, Carpp Lindsay N, Gelderblom Huub C, Huang Yunda, Benkeser David, Gilbert Peter B
Biostatistics Division; Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, USA.
Vaccine and Infectious Disease Division; Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
iScience. 2023 Aug 9;26(9):107595. doi: 10.1016/j.isci.2023.107595. eCollection 2023 Sep 15.
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.
联合单克隆广泛中和抗体（bnAb）方案正在进行预防HIV的临床开发，因此需要更多关于bnAb对循环病毒的中和效力/广度的知识。威廉姆森等人（2021年）描述了一种软件工具，即NAb面板超级学习预测（SLAPNAP），该工具可应用于任何具有足够中和数据的HIV bnAb方案，这些数据针对洛斯阿拉莫斯国家实验室的编译、中和和统计Nab面板存储库中的一组病毒。SLAPNAP根据预测的中和抗性为Env序列生成蛋白质组抗体抗性（PAR）评分，并估计Env氨基酸特征的可变重要性。我们应用SLAPNAP比较正在进行临床试验的HIV bnAb方案，发现由于纳入了Env序列的PAR评分，下游筛选分析的效力得到提高，并且在比较bnAb方案的中和效力/广度时精度增加，因为与中和结果相比，可用的样本量要大得多。SLAPNAP显著改善了bnAb方案的表征、排名和向下选择。
