Pfizer, Inc., Andover, MA 01810, United States.
J Immunol Methods. 2013 Jun 28;392(1-2):12-23. doi: 10.1016/j.jim.2013.02.019. Epub 2013 Mar 13.
Response surface methods (RSM) combined with a steepest ascent approach is a powerful technique to optimize assay performance. In this case, a ligand-binding assay (LBA) to quantitate a peptide biotherapeutic was optimized for improved sensitivity using this technique. Conditions were elucidated to enable pg/mL quantitation of the peptide in human plasma using steepest ascent to efficiently optimize assay factors. Instead of relying solely on assay development experience and intuition to improve assay sensitivity, this systematic approach takes advantage of a predictive mathematical model generated through response surface methods that defines a specific path towards greater predicted assay sensitivity. The actual response observed along the steepest ascent path was in good agreement with the model for several steps, until reagent concentrations moved beyond the physical limits of the system, and model breakdown occurred. RSM combined with steepest ascent method proved a useful tool for sensitivity optimization in three ways: (1) The required LBA sensitivity performance (approximately 200 pg/mL), measured as a signal-to-noise ratio (SNR) at the targeted lower limit of quantitation (LLOQ), was efficiently achieved in only two optimization experiments; (2) Steepest ascent confirmed that the desired sensitivity was found within the initial RSM design space, and no further gain in sensitivity was found venturing beyond this design space along the steepest ascent path; (3) The desired assay sensitivity was maintained over a reasonable range of reagent concentrations along the steepest ascent path, indicating assay robustness for this parameter.
响应面法(RSM)与最陡上升法相结合是优化分析性能的强大技术。在这种情况下,使用该技术优化了一种用于定量测定肽生物治疗剂的配体结合分析(LBA),以提高灵敏度。阐明了条件,以便能够使用最陡上升法在人血浆中对肽进行 pg/mL 定量,从而有效地优化分析因子。这种系统方法不是仅依赖于分析开发经验和直觉来提高分析灵敏度,而是利用通过响应面方法生成的预测数学模型,该模型定义了一条通往更高预测分析灵敏度的特定路径。在沿着最陡上升路径观察到的实际响应与模型在几个步骤中非常吻合,直到试剂浓度超出系统的物理极限,并且模型崩溃。RSM 与最陡上升方法相结合,通过以下三种方式证明了在灵敏度优化方面非常有用:(1)所需的 LBA 灵敏度性能(以目标定量下限(LLOQ)处的信噪比(SNR)测量),仅通过两个优化实验即可有效地实现;(2)最陡上升法证实了在初始 RSM 设计空间内找到了所需的灵敏度,并且沿着最陡上升路径超出该设计空间没有进一步提高灵敏度;(3)在沿着最陡上升路径的合理试剂浓度范围内保持了所需的分析灵敏度,表明该参数的分析稳健性。