Abstract：
Biologics, especially monoclonal antibodies (mAbs), are an increasingly important part of the drug discovery and development portfolio across the pharmaceutical industry. To enable robust demonstration of pillars 1 and 2 [1] for mAbs, specialised assays are required to measure the complex interactions between mAb and target. This is especially important for the interpretation of soluble target interactions. In some instances, multiple assays with overlapping purposes (e.g., developing both complex and total assays) have been developed. In retrospect, these efforts may have led to excessive time and resources spent in assay development and the generation of data that is contradictory or misleading. Our recommendation is to invest resources early into the development of total assays for both mAb and target. Free target assay data may be inaccurate and report higher levels of free target than are present in the sample at collection due to re-equilibrium during measurement. Total assay formats are inherently less sensitive to the effects of sample preparation, assay conditions, and re-equilibration than free or complex assays. It is acknowledged that pathology/pharmacology is ultimately driven by the free target and knowledge of its dynamics are critical. However, generation of appropriate total target data and using model-based estimation of free target concentrations is a more robust approach than utilisation of direct assay derived estimates. Where free data are utilised, the potential biases should be prospectively considered when developing the assay and utilising the data for quantitative analyses.
摘要：
生物制品,特别是单克隆抗体（mAb），是整个制药行业药物发现和开发组合中越来越重要的一部分。为了对MABS的支柱1和2[1]进行强有力的演示，需要专门的测定法来测量mAb和靶标之间的复杂相互作用。这对于可溶性靶标相互作用的解释尤其重要。在某些情况下,具有重叠目的的多个测定(例如，开发了复杂和全面的检测方法）。回想起来，这些努力可能导致在试验开发和数据生成上花费了过多的时间和资源。我们的建议是尽早将资源投入到mAb和靶标的总测定的开发中。游离靶标测定数据可能是不准确的,并且由于测量期间的重新平衡,报告的游离靶标水平高于收集时样品中存在的水平。总测定格式本质上对样品制备的影响不太敏感，测定条件，和重新平衡比自由或复杂的测定。众所周知，病理学/药理学最终是由自由靶标驱动的，并且对其动力学的了解至关重要。然而,生成适当的总目标数据并使用基于模型的游离目标浓度估计是比利用直接测定得出的估计更可靠的方法。在使用免费数据的地方，在开发检测方法和利用数据进行定量分析时,应前瞻性地考虑潜在偏差.
