Ana S. Simaria

Allogeneic cell therapy bioprocess economics and optimization: Single‐use cell expansion technologies

For allogeneic cell therapies to reach their therapeutic potential, challenges related to achieving scalable and robust manufacturing processes will need to be addressed. A particular challenge is producing lot‐sizes capable of meeting commercial demands of up to 109 cells/dose for large patient numbers due to the current limitations of expansion technologies. This article describes the application of a decisional tool to identify the most cost‐effective expansion technologies for different scales of production as well as current gaps in the technology capabilities for allogeneic cell therapy manufacture. The tool integrates bioprocess economics with optimization to assess the economic competitiveness of planar and microcarrier‐based cell expansion technologies. Visualization methods were used to identify the production scales where planar technologies will cease to be cost‐effective and where microcarrier‐based bioreactors become the only option. The tool outputs also predict that for the industry to be sustainable for high demand scenarios, significant increases will likely be needed in the performance capabilities of microcarrier‐based systems. These data are presented using a technology S‐curve as well as windows of operation to identify the combination of cell productivities and scale of single‐use bioreactors required to meet future lot sizes. The modeling insights can be used to identify where future R&D investment should be focused to improve the performance of the most promising technologies so that they become a robust and scalable option that enables the cell therapy industry reach commercially relevant lot sizes. The tool outputs can facilitate decision‐making very early on in development and be used to predict, and better manage, the risk of process changes needed as products proceed through the development pathway. Biotechnol. Bioeng. 2014;111: 69–83.

Allogeneic cell therapy bioprocess economics and optimization: downstream processing decisions

AIM To develop a decisional tool to identify the most cost effective process flowsheets for allogeneic cell therapies across a range of production scales. MATERIALS & METHODS A bioprocess economics and optimization tool was built to assess competing cell expansion and downstream processing (DSP) technologies. RESULTS Tangential flow filtration was generally more cost-effective for the lower cells/lot achieved in planar technologies and fluidized bed centrifugation became the only feasible option for handling large bioreactor outputs. DSP bottlenecks were observed at large commercial lot sizes requiring multiple large bioreactors. The DSP contribution to the cost of goods/dose ranged between 20-55%, and 50-80% for planar and bioreactor flowsheets, respectively. CONCLUSION This analysis can facilitate early decision-making during process development.

Designing cost‐effective biopharmaceutical facilities using mixed‐integer optimization

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.

Decisional tool to assess current and future process robustness in an antibody purification facility

Increases in cell culture titers in existing facilities have prompted efforts to identify strategies that alleviate purification bottlenecks while controlling costs. This article describes the application of a database‐driven dynamic simulation tool to identify optimal purification sizing strategies and visualize their robustness to future titer increases. The tool harnessed the benefits of MySQL to capture the process, business, and risk features of multiple purification options and better manage the large datasets required for uncertainty analysis and optimization. The database was linked to a discrete‐event simulation engine so as to model the dynamic features of biopharmaceutical manufacture and impact of resource constraints. For a given titer, the tool performed brute force optimization so as to identify optimal purification sizing strategies that minimized the batch material cost while maintaining the schedule. The tool was applied to industrial case studies based on a platform monoclonal antibody purification process in a multisuite clinical scale manufacturing facility. The case studies assessed the robustness of optimal strategies to batch‐to‐batch titer variability and extended this to assess the long‐term fit of the platform process as titers increase from 1 to 10 g/L, given a range of equipment sizes available to enable scale intensification efforts. Novel visualization plots consisting of multiple Pareto frontiers with tie‐lines connecting the position of optimal configurations over a given titer range were constructed. These enabled rapid identification of robust purification configurations given titer fluctuations and the facility limit that the purification suites could handle in terms of the maximum titer and hence harvest load.

Optimising chromatography strategies of antibody purification processes by mixed integer fractional programming techniques

Abstract The strategies employed in chromatography steps play a key role in downstream processes for monoclonal antibody (mAb) manufacture. This work addresses the integrated optimisation of chromatography step sequencing and column sizing in mAb purification processes. Chromatography sequencing decisions include the resin selection at each typical step, while the column sizing decisions include the number of columns, the column diameter and bed height, and number of cycles per batch. A mixed integer nonlinear programming (MINLP) model was developed and then reformulated as a mixed integer linear fractional programming (MILFP) model. A literature approach, the Dinkelbach algorithm, was adopted as the solution method for the MILFP model. Finally, an industrially-relevant case study was investigated for the applicability of the proposed models and approaches.

Closed-loop optimization of chromatography column sizing strategies in biopharmaceutical manufacture.

BACKGROUND This paper considers a real-world optimization problem involving the identification of cost-effective equipment sizing strategies for the sequence of chromatography steps employed to purify biopharmaceuticals. Tackling this problem requires solving a combinatorial optimization problem subject to multiple constraints, uncertain parameters, and time-consuming fitness evaluations. RESULTS An industrially-relevant case study is used to illustrate that evolutionary algorithms can identify chromatography sizing strategies with significant improvements in performance criteria related to process cost, time and product waste over the base case. The results demonstrate also that evolutionary algorithms perform best when infeasible solutions are repaired intelligently, the population size is set appropriately, and elitism is combined with a low number of Monte Carlo trials (needed to account for uncertainty). Adopting this setup turns out to be more important for scenarios where less time is available for the purification process. Finally, a data-visualization tool is employed to illustrate how user preferences can be accounted for when it comes to selecting a sizing strategy to be implemented in a real industrial setting. CONCLUSION This work demonstrates that closed-loop evolutionary optimization, when tuned properly and combined with a detailed manufacturing cost model, acts as a powerful decisional tool for the identification of cost-effective purification strategies.

Mixed integer optimisation of antibody purification processes

Abstract Chromatographic operations are identified as critical steps in a monoclonal antibody (mAb) purification process and can represent a significant proportion of the purification material costs. The optimisation of chromatography equipment sizing strategies is therefore crucial to improve the cost-effectiveness of mAb manufacture. In this work, a mixed-integer linear programming model (MILP) was developed to determine the optimal chromatography column sizing decisions, so as to minimise the cost of goods per gram (COG/g) of the whole mAb manufacturing process. Modelling challenges related with non-linearities involving the multiplication of decision variables were addressed by the use of linearisation techniques allowing the resulting model to determine global process performance metrics (e.g. chromatography processing time, COG/g). The application of the MILP model to an industrially-relevant case study combined with the use of visualisation methods proved to be a valuable tool to explore the characteristics of the optimal sizing strategies across different scenarios and to facilitate decision-making.

Efficient discovery of chromatography equipment sizing strategies for antibody purification processes using evolutionary computing

This paper considers a real-world optimization problem involving the discovery of cost-effective equipment sizing strategies for the chromatography technique employed to purify biopharmaceuticals. Tackling this problem requires solving a combinatorial optimization problem subject to multiple constraints, uncertain parameters (and thus noise), and time-consuming fitness evaluations. After introducing this problem, an industrially-relevant case study is used to demonstrate that evolutionary algorithms perform best when infeasible solutions are repaired intelligently, the population size is set appropriately, and elitism is combined with a low number of Monte Carlo trials (needed to account for uncertainty). Adopting this setup turns out to be more important for scenarios where less time is available for the purification process.

An Optimisation-based Approach for Biopharmaceutical Manufacturing

Abstract This work addresses the integrated optimisation of upstream and downstream processing strategies in the manufacturing processes of monoclonal antibodies (mAbs). In the upstream processing (USP), the bioreactor sizing strategies are considered, while in the downstream processing (DSP), the chromatography sequencing and column sizing strategies are optimised, including the decisions on the resin selection, the number of columns, the column diameter and bed height, and number of cycles per batch. Also, the product’s purity requirement is considered, in which the host cell protein level in the final product is examined. A mixed integer linear programming (MILP) model is developed with the objective function to minimise the annual total cost of goods (COG), involving both direct and indirect costs. Finally, an example with different USP and DSP ratios are studied.

Designing multi-product biopharmaceutical facilities using evolutionary algorithms

An evolutionary algorithm based approach is presented to address the problem of designing flexible and cost-effective multi-product biopharmaceutical facilities. For a given portfolio of products with different demands, upstream yields and impurity levels, the proposed approach is able to tackle multiple decisions simultaneously so as to minimise cost of goods, namely the: optimal ratio of upstream to downstream trains, sequence of purification operations to be used for each product and equipment sizing strategy of each operation. The evolutionary algorithm is linked to a detailed process economics model to evaluate the multiple financial and operational outputs of each string. An industrially-relevant case study is presented that focuses on the design of manufacturing facilities for the production of monoclonal antibodies at different phases of clinical development. The evolutionary algorithm was found to search the decision space efficiently, identify the most promising solutions and provide novel insights on competing sequences.