Abstract：
The propensity for some monoclonal antibodies (mAbs) to aggregate at physiological and manufacturing pH values can prevent their use as therapeutic molecules or delay time to market. Consequently, developability assessments are essential to select optimum candidates, or inform on mitigation strategies to avoid potential late-stage failures. These studies are typically performed in a range of buffer solutions because factors such as pH can dramatically alter the aggregation propensity of the test mAbs (up to 100-fold in extreme cases). A computational method capable of robustly predicting the aggregation propensity at the pH values of common storage buffers would have substantial value. Here, we describe a mAb aggregation prediction tool (MAPT) that builds on our previously published isotype-dependent, charge-based model of aggregation. We show that the addition of a homology model-derived hydrophobicity descriptor to our electrostatic aggregation model enabled the generation of a robust mAb developability indicator. To contextualize our aggregation scoring system, we analyzed 97 clinical-stage therapeutic mAbs. To further validate our approach, we focused on six mAbs (infliximab, tocilizumab, rituximab, CNTO607, MEDI1912 and MEDI1912_STT) which have been reported to cover a large range of aggregation propensities. The different aggregation propensities of the case study molecules at neutral and slightly acidic pH were correctly predicted, verifying the utility of our computational method.
摘要：
一些单克隆抗体(mAb)在生理和制造pH值下聚集的倾向可阻止其用作治疗性分子或延迟上市时间。因此,发展性评估对于选择最佳候选人至关重要，或告知缓解策略，以避免潜在的后期故障。这些研究通常在一系列缓冲溶液中进行,因为诸如pH的因素可以显著改变测试mAb的聚集倾向(在极端情况下高达100倍)。能够可靠地预测在常见储存缓冲液的pH值下的聚集倾向的计算方法将具有实质价值。这里,我们描述了一种mAb聚集预测工具（MAPT），它建立在我们之前发表的同种型依赖的基础上，基于电荷的聚合模型。我们表明，将同源模型衍生的疏水性描述符添加到我们的静电聚集模型中，可以生成强大的mAb显影性指标。为了将我们的聚合评分系统上下文化，我们分析了97个临床期治疗性单克隆抗体.为了进一步验证我们的方法，我们专注于六种单克隆抗体(英夫利昔单抗，托珠单抗,利妥昔单抗,CNTO607，MEDI1912和MEDI1912_STT）已被报道涵盖了大范围的聚集倾向。正确预测了案例研究分子在中性和微酸性pH下的不同聚集倾向，验证了我们计算方法的实用性。
