Department of Pharmacokinetics and Drug Metabolism, Amgen Inc, South San Francisco, CA, USA.
Therapeutic Discovery, Amgen, Thousand Oaks, CA, USA.
MAbs. 2023 Jan-Dec;15(1):2263926. doi: 10.1080/19420862.2023.2263926. Epub 2023 Oct 12.
In this investigation, we tested the hypothesis that a physiologically based pharmacokinetic (PBPK) model incorporating measured metrics of off-target binding can largely explain the inter-antibody variability in monoclonal antibody (mAb) pharmacokinetics (PK). A diverse panel of 83 mAbs was evaluated for PK in wild-type mice and subjected to 10 assays to measure major physiochemical attributes. After excluding for target-mediated elimination and immunogenicity, 56 of the remaining mAbs with an eight-fold variability in the area under the curve (: 1.74 × 10 -1.38 × 10 ng∙h/mL) and 10-fold difference in clearance (2.55-26.4 mL/day/kg) formed the training set for this investigation. Using a PBPK framework, mAb-dependent coefficients 1 and 2 modulating pinocytosis rate and convective transport, respectively, were estimated for each mAb with mostly good precision (coefficient of variation (CV%) <30%). 1 was estimated to be the mean and standard deviation of 0.961 ± 0.593, and 2 was estimated to be 2.13 ± 2.62. Using principal component analysis to correlate the regressed values of 1/2 versus the multidimensional dataset composed of our panel of assays, we found that heparin chromatography retention time emerged as the predictive covariate to the mAb-specific 1, whereas 2 variability cannot be well explained by these assays. A sigmoidal relationship between 1 and the identified covariate was incorporated within the PBPK framework. A sensitivity analysis suggested plasma concentrations to be most sensitive to 1 when 1 > 1. The predictive utility of the developed PBPK model was evaluated against a separate panel of 14 mAbs biased toward high clearance, among which area under the curve of PK data of 12 mAbs was predicted within 2.5-fold error, and the positive and negative predictive values for clearance prediction were 85% and 100%, respectively. MAb heparin chromatography assay output allowed identification of mAb candidates with unfavorable PK.
在这项研究中，我们检验了一个假设，即结合了非靶点结合的生理相关药代动力学（PBPK）模型可以在很大程度上解释单克隆抗体（mAb）药代动力学（PK）的抗体间变异性。评估了一个多样化的 83 个 mAb 群体在野生型小鼠中的 PK，并进行了 10 项测定以测量主要的物理化学属性。在排除了靶介导的消除和免疫原性之后，剩余的 56 个 mAb 具有 8 倍的曲线下面积变异性（ AUC：1.74×10 -1.38×10ng·h/mL）和 10 倍的清除率差异（2.55-26.4mL/day/kg），形成了本研究的训练集。使用 PBPK 框架，分别对每个 mAb 估计了调节胞吞作用速率和对流转运的 mAb 相关系数 1 和 2，精度大多较好（变异系数（CV%）<30%）。1 被估计为 0.961±0.593 的平均值和标准差，2 被估计为 2.13±2.62。使用主成分分析将回归值 1/2 与由我们的测定面板组成的多维数据集相关联，我们发现肝素色谱保留时间成为与 mAb 特异性 1 相关的预测协变量，而 2 的变异性不能很好地用这些测定来解释。在 PBPK 框架中，1 与鉴定的协变量之间存在一种 S 型关系。敏感性分析表明，当 1>1 时，1 对血浆浓度最敏感。通过对 14 个偏向高清除率的 mAb 的独立面板进行评估，验证了所开发的 PBPK 模型的预测能力，其中 12 个 mAb 的 PK 数据的 AUC 在 2.5 倍误差范围内进行了预测，清除率预测的阳性和阴性预测值分别为 85%和 100%。mAb 肝素色谱测定结果可以识别具有不利 PK 的 mAb 候选物。
