Abstract
Chromatography operations are identified as critical steps in a monoclonal antibody (mAb) purification process and can represent a significant proportion of the purification material costs. This becomes even more critical with increasing product titers that result in higher mass loads onto chromatography columns, potentially causing capacity bottlenecks. In this work, a mixed-integer nonlinear programming (MINLP) model was created and applied to an industrially relevant case study to optimize the design of a facility by determining the most cost-effective chromatography equipment sizing strategies for the production of mAbs. Furthermore, the model was extended to evaluate the ability of a fixed facility to cope with higher product titers up to 15 g/L. Examination of the characteristics of the optimal chromatography sizing strategies across different titer values enabled the identification of the maximum titer that the facility could handle using a sequence of single column chromatography steps as well as multi-column steps. The critical titer levels for different ratios of upstream to dowstream trains where multiple parallel columns per step resulted in the removal of facility bottlenecks were identified. Different facility configurations in terms of number of upstream trains were considered and the trade-off between their cost and ability to handle higher titers was analyzed. The case study insights demonstrate that the proposed modeling approach, combining MINLP models with visualization tools, is a valuable decision-support tool for the design of cost-effective facility configurations and to aid facility fit decisions. 2013.
Highlights
The number of variables in the MINLPFacility-fit model depended on the solution of the MINLPDesign model, and its CPU time was tens of seconds for all scenarios investigated in the case study
With the increasing number of upstream processing (USP) trains, the optimal solutions were characterized by using similar column volumes but running for fewer cycles to shorten the downstream processing (DSP) time such that it fitted within tighter DSP windows
A trade-off exists between the Comparison of the characteristics of the optimal solutions provided by the mixedinteger nonlinear programming (MINLP) model for the different USP:DSP scenarios in terms of (a) column volume and number of cycles, (b) cost of goods (COG)/g with corresponding breakdown, (c) product output and number of batches manufactured per year
Summary
As the monoclonal antibody (mAb) sector has matured, it has become critical to rapidly identify the most costeffective purification processes that can handle increasing upstream productivities in a timely manner and overcome existing purification bottlenecks.[1,2,3] Chromatography operations are identified as critical steps in a mAb purification process and can represent a significant proportion of the purification material costs, due to the use of expensive affinity matrices as well as the high amounts of. The following parameters are inputs: the process sequence of a mAb product, the annual demand, the product titer, the ratio of USP to DSP trains, the key operating parameters of the chromatography operations (e.g., yield, linear velocity, buffer usage, resin dynamic binding capacity), the processing times of non-chromatography unit operations, cost data (e.g., reference equipment costs, labor rate, resin, buffer, and media prices), the column diameter and height candidates, and the maximum number of cycles and columns Given these inputs, the goal is to determine the column sizing strategies (i.e., column diameter and height, the number of cycles, number of columns at each step), the number of completed batches, the total product output and the total annual cost so as to minimize COG/g. The equipment cost was used to calculate the capital investment value
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