There is growing interest in the use of algorithms to objectively compare near UV spectra of protein biopharmaceuticals in a regulated environment. Such use will require that the methods be validated, with ICH Q2(R1) currently being the key document. A key aspect of such validation is to understand how robust the method is to experimental variation. Noise-free simulated spectra, obtained by fitting multiple Gaussian peaks to experimental data obtained from a pharmaceutical protein, were used to assess the robustness of several algorithms in response to spectral data "imperfections". Sources and magnitudes of these imperfections were derived from published inter-laboratory studies. Spectral noise, wavelength calibration errors, intensity variation, and spectral offset errors were âtitratedâ into the noise-free simulated spectrum and imperfect data sets were compared with the simulated data using a variety of published algorithms, including Pearson, Prestrelski, and derivative correlation algorithms, and spectral overlap, spectral difference and weighted spectral difference methods, to understand how robust outputs are to imperfect data. Algorithm were assessed by comparing their sensitivity to imperfect data against the pairwise statistical variation between 20 replicate spectra.

中文翻译：

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EXPRESS：不完美数据对比较近紫外圆二色光谱算法性能的影响

人们对使用算法在受监管的环境中客观地比较蛋白质生物药物的近紫外光谱越来越感兴趣。这种使用需要对方法进行验证，目前 ICH Q2(R1) 是关键文件。这种验证的一个关键方面是了解该方法对实验变化的稳健性。通过将多个高斯峰与从药物蛋白质获得的实验数据拟合而获得的无噪声模拟光谱用于评估响应光谱数据“缺陷”的几种算法的稳健性。这些缺陷的来源和程度来自已发表的实验室间研究。光谱噪声、波长校准误差、强度变化、并且将频谱偏移误差“滴定”到无噪声模拟频谱中，并使用各种已发布的算法（包括 Pearson、Prestrelski 和导数相关算法，以及频谱重叠、频谱差异和加权频谱差异）将不完美的数据集与模拟数据进行比较方法，以了解输出对不完美数据的鲁棒性。通过比较它们对不完美数据的敏感性与 20 个重复光谱之间的成对统计变化来评估算法。