Biostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States of Amerrica.
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States of Amerrica.
PLoS One. 2024 Sep 6;19(9):e0310042. doi: 10.1371/journal.pone.0310042. eCollection 2024.
Combination monoclonal broadly neutralizing antibodies (bnAbs) are currently being developed for preventing HIV-1 acquisition. 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 80% concentration < 1 μg/mL). For continuous outcomes, the CP approach performs nearly as well as the PC approach when the individual-bnAb prediction algorithms have strong performance, 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.
组合型单克隆广泛中和抗体（bnAbs）目前正在被开发用于预防 HIV-1 感染。最近的工作集中在使用 Env 序列特征预测针对 HIV-1 假病毒的单个 bnAbs 和组合方案的体外中和效力。为了预测针对特定 HIV-1 假病毒的体外组合方案的中和效力，以前的方法应用数学模型来组合单个 bnAbs 的中和作用，并预测这种组合中和值；我们称这种方法为“先组合后预测（CP）”方法。然而，一些单个 bnAbs 的预测性能已经超过了组合方案，这就引出了另一种可能性：组合单个 bnAbs 的预测值，并使用这些值来预测组合方案的中和作用；我们称这种方法为“先预测后组合（PC）”方法。我们在模拟数据和 Los Alamos 国家实验室的 Compile、Neutralize 和 Tally NAb 面板库的数据中探索了这两种方法。当感兴趣的中和结果是二进制的（例如，中和敏感性，定义为抑制 80%浓度<1μg/ml）时，CP 方法优于 PC 方法。对于连续结果，当个体 bnAb 预测算法具有良好的性能时，CP 方法的性能几乎与 PC 方法一样好，而当个体 bnAb 预测算法的性能较差时，CP 方法优于 PC 方法。在缺乏针对抗体组合的体外中和数据的情况下，当构建新型抗体组合的预测模型时，可以利用这些知识，这反过来将有助于评估和选择这些抗体组合进入预防功效试验。
