Abstract Protein therapeutics are vitally important clinically and commercially, with monoclonal antibody (mAb) therapeutic sales alone accounting for $115 billion in revenue for 2018.[1] In order for these therapeutics to be safe and efficacious, their protein components must maintain their high order structure (HOS), which includes retaining their three-dimensional fold and not forming aggregates. As demonstrated in the recent NISTmAb Interlaboratory nuclear magnetic resonance (NMR) Study[2], NMR spectroscopy is a robust and precise approach to address this HOS measurement need. Using the NISTmAb study data, we benchmark a procedure for automated outlier detection used to identify spectra that are not of sufficient quality for further automated analysis. When applied to a diverse collection of all 252 1H,13C gHSQC spectra from the study, a recursive version of the automated procedure performed comparably to visual analysis, and identified three outlier cases that were missed by the human analyst. In total, this method represents a distinct advance in chemometric detection of outliers due to variation in both measurement and sample.

中文翻译：

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对 2D-NMR 光谱进行化学计量异常值分类，以实现蛋白质治疗药物的高阶结构表征


摘要 蛋白质疗法在临床和商业上都至关重要，单克隆抗体 (mAb) 疗法的销售额在 2018 年就达到了 1150 亿美元的收入。 [1]为了使这些疗法安全有效，它们的蛋白质成分必须保持其高阶结构（HOS），其中包括保留其三维折叠且不形成聚集体。正如最近的 NISTmAb 实验室间核磁共振 (NMR) 研究 [2] 所证明的那样，NMR 波谱是满足这种 HOS 测量需求的稳健且精确的方法。使用 NISTmAb 研究数据，我们对自动异常值检测程序进行了基准测试，用于识别质量不足以进行进一步自动分析的光谱。当应用于研究中所有 252 个 1H,13C gHSQC 光谱的不同集合时，自动化程序的递归版本的性能与视觉分析相当，并识别出人类分析人员遗漏的三个异常情况。总的来说，由于测量和样本的变化，该方法代表了化学计量学检测异常值的明显进步。