(406e) Optimal Design of Antibody Downstream Process of Biopharmaceutical Manufacturing

With the increasing market potential of therapeutic monoclonal antibodies (mAbs), it has become critical to rapidly identify the most cost-effective purification processes that can handle increasing upstream productivities in a timely manner and overcome existing purification bottlenecks. Chromatography operations are identified as critical steps in a mAb purification process and can represent a significant proportion of the purification material costs, particularly due to the use of expensive affinity matrices as well as the large amount of buffer reagents required. A key strategic decision relates to the sequence of chromatography steps, which affect the not only the investment and operating cost for the chromatography steps, but also the overall downstream process. Another key decision is the chromatography sizing strategies and this is complicated by the dependencies between steps. There are a large number of possible permutations and trade-offs related to the packed-bed chromatography operations, such as opting for a smaller column run for several cycles to reduce resin costs versus a large column run for fewer cycles to save time.

The aim of this work is to develop an optimisation-based business decision-support framework for the optimal design of chromatography purification steps in mAb manufacture. In this work, a typical mAb purification process with a sequence of three packed-bed chromatography steps was considered. Firstly, a mixed integer nonlinear programming (MINLP) model was developed to determine the optimal chromatography sequencing decisions (resin at each chromatography step) and chromatography column sizing decisions (number of columns, column diameter, column bed height, number of cycles at each chromatography step), so as to minimise the cost of goods per gram (COG/g) of the whole mAb manufacturing process. Due to the computational difficulties to obtain the global optimum of the nonlinear optimisation model, the proposed MINLP model was reformulated as a mixed integer linear fractional programming (MILFP) model using exact linearisation techniques by introducing a number of auxiliary variables and constraints, which was solved by an adopted literature solution approach. An industrially-relevant example with different configurations of upstream and downstream trains was investigated, and the computational results proved the applicability of the proposed models and approaches.