Development of machine learning tools to improve the developability of therapeutic antibodies.

抗体是一种流行的治疗形式，因为它们能够特异性靶向任何分子。已经成功地开发了治疗性抗体以靶向多种疾病，包括癌症和传染病。然而，治疗性抗体开发是一项复杂且昂贵的任务。即使候选物是强且特异性的结合剂，其也可能遭受可开发性问题-例如高免疫原性、不稳定性、自缔合、高粘度、多特异性或表达差-这可能使其作为治疗剂是站不住脚的。因此，在药物发现管道中尽早识别和标记任何问题非常重要。由于抗体的基于结构的生物物理性质，出现了许多这样的问题。现有的工具能够计算这些特性并标记这些可开发性问题，例如治疗性抗体分析仪（TAP）。然而，目前还没有能够对这些标记的候选物进行多目标结构优化的工具，在保留抗原结合特性的同时消除可开发性问题。这种工具的开发将证明在防止被标记为不可开发的高亲和力抗体从药物发现管道中被丢弃方面是非常宝贵的。这将节省资金，有助于使主要疗法多样化，并可能解锁新的靶点。该项目旨在创建这样一种工具，能够建议突变以克服抗体的可开发性问题，同时还保留对其靶表位的结合亲和力。对于这样的多重优化问题，机器学习（ML）模型将非常适合。开发的ML模型将利用最先进的深度学习技术，如等变图神经网络和变压器。抗体（例如ABlooper）的计算结构建模准确性的进步允许采取结构感知方法。基于对突变空间的限制的规则也可以被整合到模型中以防止抗原结合亲和力的降低。该项目属于EPSRC化学生物学和生物化学主题的福尔斯。该项目是基于牛津蛋白质信息组，统计系与英国阿斯利康合作，由夏洛特迪恩教授学术监督，由Rebecca Croassdale-Wood博士工业监督。阿斯利康提供抗体合成和分析能力，允许模型预测的实验验证。

Antibodies are a popular therapeutic format due to their ability to specifically target any molecule. Therapeutic antibodies have been successfully developed to target a variety of diseases, including cancer and infectious diseases. However, therapeutic antibody development is a complex and expensive task. Even if a candidate is a strong and specific binder, it may suffer from developability issues - such as high immunogenicity, instability, self-association, high viscosity, polyspecificity, or poor expression - which may make it untenable as a therapeutic. It is therefore important to recognise and flag any issues as early as possible in the drug discovery pipeline. Many such issues arise due to structure-based biophysical properties of the antibody. Current tools exist which are able to calculate these properties and flag such developability issues, such as the Therapeutic Antibody Profiler (TAP). However, there is currently no tool capable of multi-objective structural optimisation of these flagged candidates, removing developability issues while retaining antigen binding properties. Development of such a tool would prove invaluable in preventing high affinity antibodies that are flagged as undevelopable from being discarded from the drug discovery pipeline. This would save money, help diversify lead therapeutics, and potentially unlocking new targets. This project aims to create such a tool, capable of suggesting mutations to overcome developability issues for an antibody, while also retaining binding affinity for its target epitope. For a multi-optimisation problem like this, a machine learning (ML) model would be well suited. The ML model developed will take advantage of state-of-the-art deep learning advances, such as equivariant graph neural networks and transformers. Advances in the accuracy of computational structural modelling of antibodies (e.g. ABlooper) allows for a structure-aware approach to be taken. Rules based limitations on the mutational space can also be integrated into the model to prevent a reduction in antigen binding affinity. The project falls within the EPSRC chemical biology and biological chemistry theme. The project is based at the Oxford Protein Informatics Group, Department of Statistics in collaboration with AstraZeneca UK, with academic supervision by Professor Charlotte Deane, and industrial supervision by Dr Rebecca Croassdale-Wood. AstraZeneca are providing antibody synthesis and assaying capabilities, allowing experimental validation of model predictions.