Development of monoclonal antibody formulations to decrease aggregation during long-term storage using molecular simulations

单克隆抗体（mab）等治疗性蛋白已被批准用于治疗人类各种疾病。它们通常通过皮下注射给药给病人，这需要高浓度制剂，以便在有限的体积内达到剂量要求。在如此高的浓度下，蛋白质很可能在几天到几周的短时间内形成聚集体。然而，对于商业化的治疗性蛋白质，需要超过12个月的保质期。因此，单克隆抗体在生产和储存过程中的稳定性至关重要。减少聚合倾向是成功的关键。添加辅料（如离子、游离氨基酸、糖和小有机分子）可用于在制造和储存过程中稳定单克隆抗体。通过改变赋形剂分子的种类和浓度，可以改变其稳定性。最近对配方稳定的应用进行了研究，但其分子机制尚未发现。从先驱者的实验数据中，我们知道某些氨基酸被用来减少有吸引力的蛋白质-蛋白质相互作用（PPIs），从而降低粘度并提高稳定性。但进一步了解辅料如何影响高浓度单克隆抗体的黏度，作为PPIs的功能，对于处理基于蛋白质的治疗方法至关重要。本项目将建立临床批准的人单克隆抗体的同源性模型，然后利用原子共溶剂分子动力学（MD）模拟，重点设计和优化易于聚集的治疗性单克隆抗体的配方。共溶剂MD模拟是用于预测和表征蛋白质变构结合位点的简单而有效的技术，但其在配方开发中的应用仍未得到充分探索。模拟可以给出非键相互作用的详细图像，以及在原子水平上稳定蛋白质的键相互作用。在该项目中，co溶剂分析工具包（CAT，最近在Bronowska的实验室开发）将用于分析在不同温度和压力范围内具有不同组合和辅料浓度的单克隆抗体的轨迹。CAT可以利用一种新型的混合经验力场评分函数对单抗与赋形剂之间的动态相互作用进行准确排序，从而识别出单抗内部容易聚集的“热点”，并通过调整赋形剂分子来减轻聚集。根据轨迹计算溶剂暴露表面积（SASA）、均方根偏差（RMSD）、径向分布因子（RDF）和每个残留物构象熵等性质，并推导出每个区域的相对聚集倾向。然后将结果与实验数据进行比较，如粒径排除色谱（SEC）、小角度x射线散射（SAXS）和动态光散射（DLS）数据。国际工业合作伙伴（Iksuda Therapeutics）的加入将提供一套抗体-药物偶联物（ADC）的实验数据，并将参与治疗性单克隆抗体和ADC改进配方的联合迭代设计。在项目期间，将深入了解原子水平上导致蛋白质聚集的因素和机制，并发现可以通过非共价相互作用破坏聚集的赋形剂。尽管该项目并不直接专注于药物发现，但它将为基于改进配方的工程蛋白的新潜在疗法开辟一条道路，这对制药行业具有吸引力。该项目与EPSRC生物物理学和软物质物理学以及化学生物学和生物化学领域保持一致。

Therapeutic proteins such as monoclonal antibodies (mAbs) have been approved for the treatment of different kinds of diseases in humans. They are often administrated into patients through subcutaneous injection which requires high concentration formulations in order to reach the dosage requirements in a limited volume. At such high concentration, the proteins are likely to form aggregates in a short period of time from days to weeks. However, for a therapeutic protein to be commercially available, a shelf life of greater than 12 months is required. Therefore, the stability of mAbs during manufacturing and storage is vital. And reducing the aggregation propensity is the key to success. Addition of excipients (such as ions, free amino acids, sugar and small organic molecules) can be used to stabilize mAbs during manufacturing and storage. By altering the types and concentration of excipient molecules the stability can be varied. Studies that focused on applications of formulation stabilisation have been carried out recently, but the molecular mechanisms are undiscovered. From pioneers' experimental data, we know that certain amino acids are used to reduce attractive protein-protein interactions (PPIs) and therefore reducing the viscosity and improving stability. But further knowledge of how excipients affect the viscosity of highly concentrated mAbs as a function of PPIs is vital for processing protein-based therapeutics.Homology models of clinically approved human mAbs will be built and then this project will focus on designing and optimizing the formulation for those aggregation-prone therapeutic monoclonal antibodies using atomistic cosolvent molecular dynamics (MD) simulations. Cosolvent MD simulations are simple and effective techniques employed for prediction and characterisation of protein allosteric binding sites, but their applications to formulation development remains underexplored. The simulations can give detailed pictures of nonbonded interactions as well as bonded interactions that stabilise proteins at the atomistic level.In the project, a Cosolvent Analysis Toolkit (CAT, recently developed in Bronowska's lab) will be used to analyse trajectories of mAbs with different combinations and concentrations of excipients in different temperature and pressure ranges. CAT can accurately rank the dynamic interactions between mAbs and excipients using a novel hybrid empirical force filed scoring function, therefore the aggregation-prone "hot spots" within mAbs can be identified and aggregation will be mitigated by adjusting the excipient molecules. Properties such as solvent-exposed surface area (SASA), root-mean-square deviation (RMSD), radial distribution factor (RDF) and per residue conformational entropy will be calculated from the trajectories and relative aggregation propensity can be derived per region. The results will then be compared with experimental data such as size-exclusion chromatography (SEC), small-angle x-ray scattering (SAXS) and dynamic light scattering (DLS) data.Inclusion of the international industrial partner (Iksuda Therapeutics) will provide a set of experimental data on antibody-drug conjugates (ADC) and will engage in joint iterative design of improved formulations for therapeutic mAbs and ADC.During the project, an in-depth understanding of the factors and mechanisms causing the protein aggregation on an atomistic level will be developed and excipients that can disrupt the aggregations by non-covalent interactions will be spotted. Although the project is not concentrating on drug discovery directly, it will lead a way to new potential therapeutics based on engineered proteins with improved formulations which is attractive to the pharmaceutical industry.The project is aligned with EPSRC Biophysics and Soft Matter Physics, and Chemical Biology and Biological Chemistry areas.