Integration of stochastic simulation with multivariate analysis: Short‐term facility fit prediction

Stonier, Adam; Pain, David; Westlake, Ashley; Hutchinson, Nicholas; Thornhill, Nina F.; Farid, Suzanne S.

doi:10.1002/btpr.1678

Cited by 12 publications

(10 citation statements)

References 33 publications

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“…Furthermore, the model is used also to determine the optimal facility fit configuration for products with higher titers. A related problem has been previously addressed by Stonier et al using a stochastic simulation framework and multivariate analysis to identify root causes of facility mismatches.…”

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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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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“…In this problem, the upstream titer and chromatography step yield with each resin are considered as uncertain parameters which follow triangular distributions. − , It is assumed that the upstream titer and step yields remain the same for different batches. The given annual demand should be met by the annual output.…”

Section: Problem Statementmentioning

confidence: 99%

Integrated Optimization of Upstream and Downstream Processing in Biopharmaceutical Manufacturing under Uncertainty: A Chance Constrained Programming Approach

Liu

Farid

Papageorgiou

2016

Ind. Eng. Chem. Res.

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This work addresses the integrated optimisation of upstream and downstream processing strategies of a monoclonal antibody (mAb) under uncertainty. In the upstream processing (USP), the bioreactor sizing strategies are optimised, while in the downstream processing (DSP), the chromatography sequencing and column sizing strategies, including the resin at each chromatography step, the number of columns, the column diameter and bed height, and the number of cycles per batch, are determined. Meanwhile, the product's purity requirement is considered. Under the uncertainties of both upstream titre and chromatography resin yields, a stochastic mixed integer linear programming (MILP) model is developed, using chance constrained programming (CCP) techniques, to minimise the total cost of goods (COG). The model is applied to an industrially-relevant example and the impact of different USP:DSP ratios is studied. The computational results of the stochastic optimisation model illustrate its advantage over the deterministic model. Also, the benefit of the integrated optimisation of both USP and DSP is demonstrated. The sensitivity analysis of both the confidence level used in the CCP model and the initial impurity level is investigated as well.

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“…With the use of PCA, they were able to predict column bed integrity failures before they occurred. PLS has also been used to predict raw material heterogeneity by comparing multivariate images to databases containing information relating to properties of the raw material being analyzed (Mollah, ; Stonier et al, ; Undey et al, ). MSPM models can predict the final values of certain key output parameters during operation.…”

Section: Prediction and Forecastingmentioning

confidence: 99%

Multivariate statistical monitoring as applied to clean‐in‐place (CIP) and steam‐in‐place (SIP) operations in biopharmaceutical manufacturing

Roy

Ündey

Mistretta

et al. 2014

Biotechnology Progress

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Multivariate statistical process monitoring (MSPM) is becoming increasingly utilized to further enhance process monitoring in the biopharmaceutical industry. MSPM can play a critical role when there are many measurements and these measurements are highly correlated, as is typical for many biopharmaceutical operations. Specifically, for processes such as cleaning-in-place (CIP) and steaming-in-place (SIP, also known as sterilization-in-place), control systems typically oversee the execution of the cycles, and verification of the outcome is based on offline assays. These offline assays add to delays and corrective actions may require additional setup times. Moreover, this conventional approach does not take interactive effects of process variables into account and cycle optimization opportunities as well as salient trends in the process may be missed. Therefore, more proactive and holistic online continued verification approaches are desirable. This article demonstrates the application of real-time MSPM to processes such as CIP and SIP with industrial examples. The proposed approach has significant potential for facilitating enhanced continuous verification, improved process understanding, abnormal situation detection, and predictive monitoring, as applied to CIP and SIP operations.

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Integration of stochastic simulation with multivariate analysis: Short‐term facility fit prediction

Cited by 12 publications

References 33 publications

Designing cost‐effective biopharmaceutical facilities using mixed‐integer optimization

Designing cost‐effective biopharmaceutical facilities using mixed‐integer optimization

Integrated Optimization of Upstream and Downstream Processing in Biopharmaceutical Manufacturing under Uncertainty: A Chance Constrained Programming Approach

Multivariate statistical monitoring as applied to clean‐in‐place (CIP) and steam‐in‐place (SIP) operations in biopharmaceutical manufacturing

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