Bioprocessing of High Concentration Protein Solutions: Quality by Digital Design Approach

有必要进行基础研究，以支持新型蛋白质疗法的工业发展，以便更方便地通过皮下注射（SC）给药。这是生物制药公司越来越重视的问题，这样患者就可以在家里给药，而不必去医院进行长时间的输液。生物加工研究面临的挑战是将所需的蛋白质溶解在可自行注射的小体积中，通常为1毫升。因此，蛋白质必须可溶至300mg /ml，并且希望液体能在2-8℃下储存2年或更长时间，无沉淀、聚集或其他不稳定性。此外，液体不能太粘稠，否则注射将需要太高的压力或可能需要太长时间才能注射。在迫使液体通过窄针进入皮肤下组织的过程中，还需要防止对蛋白质的损害。拟议的研究将开发用于工业筛选蛋白质配方的粘度和其他流动特性的方法，使用少量蛋白质。这将使降低粘度的方法得以发展。众所周知，相似的蛋白质在相似的浓度下粘度相差很大（相差两倍或更多），而改变共溶剂可以降低配方的粘度。为了实现这一目标，我们建议应用全面的流变学表征、RheoChip流变学和先进的建模作为平台，这可以被工业用于选择蛋白质和配方，以开发最终剂型，在比今天更早的阶段。这将节省许多新的蛋白质药物开发的时间和成本。该研究将建立在现有方法的基础上，这些方法已经很好地建立了水溶性聚合物和BSA溶液的流变特性，并将其应用于高浓度蛋白质生物制药溶液的生物加工和注射性。蛋白质溶液的全面流变特性尚未发表。此外，这一新知识有可能应用于工业，以改善生物制药蛋白的生产，因为在生物加工过程中可能达到高浓度，例如冷冻干燥、切向流过滤（TFF）等，并且在处理粘性溶液时可能存在困难，例如用于去除病毒的纳滤可能不切实际。该项目的可交付成果将是仪器的形式、比当前粘度测量更相关的流变表征方法和计算工具。该项目有五个工作包（wp）。WP1和WP2将侧重于开发新的使能技术。WP1的输出将是第一个高通量表征平台，用于在生物处理中遇到的流动条件下筛选蛋白质配方，同时需要最少的样品。WP2将构建浓缩蛋白流体流动预测建模的计算平台。WP3和WP4将在复杂流程下使用模型和工业相关的蛋白质解决方案（包括TFF和SC注射）对这些使能技术进行严格验证。基于从Rheo-chip获得的高通量流变数据和加工过程中蛋白质流动和蛋白质稳定性的预测建模，输出将是设计和优化（非线性）放大蛋白质生产的集成方法。WP5将把浓缩蛋白质溶液的流变特性和流动行为与辅料和/或配方条件对稀释溶液的构象稳定性和自缔合的影响联系起来。这将有助于建立高粘度和流动诱导蛋白质聚集的分子决定因素，从而合理设计高通量筛选。

There is a need for underpinning research to support industrial development of novel protein therapeutics for more convenient delivery by subcutaneous injection (SC). This is an increasing priority for biopharmaceutical companies such that patients can administer the medicines at home, rather than having to visit hospital for a lengthy infusion. The challenge for bioprocessing research is to dissolve the dose of protein required in a small volume, usually 1 ml, that can be self injected. The protein therefore must be soluble up to 300 mg/ml, and it is desirable that the liquid can be stored at 2-8 C for 2 years or more without precipitation, aggregation or other instability. In addition, the liquid must not be too viscous, otherwise the injection will require too high a pressure or may take too long to administer. There is also a need to prevent damage to the protein during the process of forcing the liquid through a narrow needle, into the tissue under the skin. The proposed research will develop methods for use by industry to screen protein formulations for viscosity and other flow properties, using small quantities of protein. This will enable methods for viscosity reduction to be developed. It is known that similar proteins differ widely (by a factor of two or more) in their viscosity at similar concentrations, and that alterations in co-solvent can reduce the viscosity of a formulation. To achieve this, we propose to apply comprehensive rheological characterisation, RheoChip rheometry, and advanced modelling as a platform, which can be used by industry to select the protein and formulation for development of the final dosage form, at an earlier stage than it is possible today. This should save time and cost in development of many new protein medicines. The research will build on existing methods, which are already well established for rheological characterisation of water soluble polymers and BSA solutions, and adapt and apply them to the bioprocessing and injectability of high concentration protein biopharmaceutical solutions. Comprehensive rheological characterisation of protein solutions has not yet been published. In addition, there is the potential for this new knowledge to be applied in industry to improve the production of biopharmaceutical proteins, as high concentrations may be reached during bioprocessing, e.g. freeze drying, tangential flow filtration (TFF) etc. and there can be difficulties in processing viscous solutions, e.g. nanofiltration for virus removal may be impractical. The deliverables of the project will be the form of instrumentation, rheological characterisation methods more relevant than current viscosity measurement, and computational tools. The project has five work packages (WPs). WP1 and WP2 will focus on development of new enabling technologies. The output of WP1 will be the first high throughput characterisation platform for screening protein formulations under the flow conditions encountered in bioprocessing while requiring minimal sample. WP2 will construct a computational platform for predictive modelling of concentrated protein fluid flows. WP3 and WP4 will critically validate these enabling technologies using both model and industrially relevant protein solutions under the complex flows, including TFF and SC injection. The output will be an integrated approach for design and optimisation of (nonlinear) scale-up protein production, based on high throughput rheological data obtained from Rheo-chip and predictive modelling of protein flows and protein stability during processing. WP5 will correlate the rheological properties and flow behaviour of concentrated protein solutions with the effects of excipients and/or formulation conditions on the conformational stability and self-association in dilute solution. This will help to establish the molecular determinants of high viscosities and flow induced protein aggregation leading to rational design of high throughput screens.