An MILP formulation for the synthesis of protein purification processes

Polykarpou, Eleftheria M.; Dalby, Paul A.; Papageorgiou, Lazaros G.

doi:10.1016/j.cherd.2011.11.021

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…For example, Vasquez-Alvarez et al model that minimizes the number of chromatography steps through optimal starting and finishing cut points. The proposed optimization model is then extended in Polykarpou et al (2012) using approximation techniques to overcome computational challenges. Note that aforementioned studies aim to minimize the number of chromatography steps but do not account for costs related to shortage (lost sale) and failures.…”

Section: Prior Workmentioning

confidence: 99%

Performance Guarantees and Optimal Purification Decisions for Engineered Proteins

Martagan

Krishnamurthy

Leland

et al. 2018

Operations Research

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We investigate protein purification operations conducted by biomanufacturers and pharmaceutical companies as part of their research and development efforts. Purification of these proteins involve unique challenges, such as, balancing the yield and purity trade-offs, dealing with uncertainty in the starting material, and estimating the impact of several interlinked decisions. We develop a Markov decision model and partition the state space into decision zones that provide managerial insights to optimize purification operations.We develop practical guidelines to quantify financial risks, and characterize the optimal operating decisions based on specific production requirements. The optimization framework has been implemented at Aldevron, a contract biomanufacturer specializing in proteins, and has resulted in 25% reduction in the total lead times and 20% reduction in the costs of protein purification operations on average.

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Section: Prior Workmentioning

confidence: 99%

Performance Guarantees and Optimal Purification Decisions for Engineered Proteins

Martagan

Krishnamurthy

Leland

et al. 2018

Operations Research

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“…Then, Polykarpou et al (2011) incorporated both starting and finishing cut-points for each chromatographic step as optimisation decision variables. Later, this work was extended by developing efficient MILP models with the discretisation approximation (Polykarpou et al, 2012a) and piecewise linearisation approximation (Polykarpou et al, 2012b) to overcome the computational difficulty of MINLP models. These models use the number of chromatography steps, purity and yield as performance metrics, but do not account for the cost of the process.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Simaria

Farid

et al. 2014

Computers & Chemical Engineering

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“…In some cases, the process synthesis optimization has also considered product loss by incorporating the decisions on the time of product collection and the start and finishing cut‐points . More recently, efficient MILP models were developed using the discretization and piecewise linearization approximation to overcome the computational expense of MINLP models. These models use the number of chromatography steps, purity, and yield as performance metrics, but do not account for overall process costs.…”

Section: Introductionmentioning

confidence: 99%

Designing cost‐effective biopharmaceutical facilities using mixed‐integer optimization

Liu

Simaria

Farid

et al. 2013

Biotechnology Progress

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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 mixedinteger 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.

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An MILP formulation for the synthesis of protein purification processes

Cited by 10 publications

References 20 publications

Performance Guarantees and Optimal Purification Decisions for Engineered Proteins

Performance Guarantees and Optimal Purification Decisions for Engineered Proteins

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

Designing cost‐effective biopharmaceutical facilities using mixed‐integer optimization

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