Next Gen MIC - concurrent selection for affinity and developability of evolved candidates

在生产过程中，基于抗体的治疗方法的聚集可能会产生问题，危及其成功开发。重要的是，虽然我们快速生成有效的mAb候选物的能力允许平台制造，但我们对序列和聚集之间的关系（在最广泛的意义上）的基本缺乏理解，这对制造更复杂的合成模式构成了重大障碍。为了解决这一未满足的需求，利兹大学的团队与阿斯利康公司一起开发了一种体内细菌聚集筛查（Ebo等人）。Nature common, 2020)有效地重新设计有问题的候选序列。我们已经证明，我们的三方β -内酰胺酶测定（tbla）能够(i)识别候选scFvs； （ii）使用定向进化改善易于聚集的序列；（iii）识别导致聚集的精确残基。在本次学习中，我们将增加tbla在生物制药开发管道和基础研究中的适用性。目的1：我们将通过使用氨苄西林耐药性和荧光分选的正交筛选，调整TPBLA以允许选择聚集抗性序列，同时保持目标结合亲和力。目的2：探测序列对目标亲和力和蛋白质聚集/稳定性的影响的能力为探索特异性和亲和性以及稳定性/亲和性权衡之间的关系打开了大门。为了做到这一点，我们将通过对tbla的深度突变测序方法生成一个大型数据集，从而量化数千个个体变体中的每个变体的“适应度”。该项目建立在学术主管和他们的工业合作者之间强有力的持续合作的基础上。所涉及的技术的广度和优秀的设施和机会，在阿斯特拉捷利康提供了一个极好的培训环境，为学生。

The aggregation of antibody-based therapeutics during manufacture can be problematic, jeopardising their successful development. Importantly, while our ability to rapidly generate effective mAb candidates allows platform manufacturing, our fundamental lack of understanding of the relationship between sequence and aggregation (in its broadest sense) is presenting a significant hurdle to the manufacture of more complex, synthetic modalities. To address this unmet need, the team at Leeds, together with Astra Zeneca, have developed an in vivo bacterial aggregation screen (Ebo et al. Nature Commun, 2020) to efficiently re-design problematic candidate sequences. We have shown that our tripartite beta-lactamase assay (TPBLA) is able to (i) identify candidate scFvs (ii) improve aggregation prone sequences using directed evolution and (iii) identify the precise residues that lead to aggregation. In this studentship we will increase the applicability of the TPBLA to biopharmaceutical development pipeline and fundamental research. Aim 1: we will adapt the TPBLA to allow selection of aggregation resistant sequences while maintaining target binding affinity by using orthogonal screens of ampicillin resistance and fluorescence sorting. Aim 2: the ability to probe the effects of sequence on target affinity and protein aggregation/stability opens the door to exploration of the relationship between specificity and avidity and stability/affinity trade-offs. To do this we will generate a large dataset by a deep mutational sequencing approach to the TPBLA whereby the "fitness" of each of thousands of individual variants is quantified. The project builds on strong on-going collaborations between the academic supervisors and their industrial collaborators. The breadth of techniques involved and the excellent facilities and opportunities at Astra Zeneca provide a superb training environment for the student.