Wafer Lucas, Kloczewiak Marek, Luo Yin
Analytical Research and Development, Biotherapeutics Pharmaceutical Sciences, Pfizer Inc., 1 Burtt Rd, Andover, Massachusetts, USA.
AAPS J. 2016 Jul;18(4):849-60. doi: 10.1208/s12248-016-9925-y. Epub 2016 May 16.
Analytical ultracentrifugation-sedimentation velocity (AUC-SV) is often used to quantify high molar mass species (HMMS) present in biopharmaceuticals. Although these species are often present in trace quantities, they have received significant attention due to their potential immunogenicity. Commonly, AUC-SV data is analyzed as a diffusion-corrected, sedimentation coefficient distribution, or c(s), using SEDFIT to numerically solve Lamm-type equations. SEDFIT also utilizes maximum entropy or Tikhonov-Phillips regularization to further allow the user to determine relevant sample information, including the number of species present, their sedimentation coefficients, and their relative abundance. However, this methodology has several, often unstated, limitations, which may impact the final analysis of protein therapeutics. These include regularization-specific effects, artificial "ripple peaks," and spurious shifts in the sedimentation coefficients. In this investigation, we experimentally verified that an explicit Bayesian approach, as implemented in SEDFIT, can largely correct for these effects. Clear guidelines on how to implement this technique and interpret the resulting data, especially for samples containing micro-heterogeneity (e.g., differential glycosylation), are also provided. In addition, we demonstrated how the Bayesian approach can be combined with F statistics to draw more accurate conclusions and rigorously exclude artifactual peaks. Numerous examples with an antibody and an antibody-drug conjugate were used to illustrate the strengths and drawbacks of each technique.
分析超速离心沉降速度法（AUC-SV）常用于定量生物制药中存在的高摩尔质量物质（HMMS）。尽管这些物质通常含量极微，但因其潜在的免疫原性而备受关注。通常，AUC-SV数据被分析为经扩散校正的沉降系数分布，即c(s)，使用SEDFIT对Lamm型方程进行数值求解。SEDFIT还利用最大熵或Tikhonov-Phillips正则化，进一步让用户确定相关的样品信息，包括存在的物质种类、它们的沉降系数及其相对丰度。然而，这种方法存在一些往往未明确说明的局限性，可能会影响蛋白质治疗药物的最终分析。这些局限性包括正则化特定效应、人为的“波纹峰”以及沉降系数的虚假偏移。在本研究中，我们通过实验验证，SEDFIT中实施的显式贝叶斯方法可在很大程度上校正这些效应。本文还提供了关于如何实施该技术以及解释所得数据的明确指南，特别是对于含有微观异质性（如差异糖基化）的样品。此外，我们展示了如何将贝叶斯方法与F统计量相结合，以得出更准确的结论并严格排除人为峰。使用了大量抗体和抗体-药物偶联物的实例来说明每种技术的优缺点。
