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Polak J, Huang Z, Sokolov M, von Stosch M, Butté A, Hodgman CE, Borys M, Khetan A. An innovative hybrid modeling approach for simultaneous prediction of cell culture process dynamics and product quality. Biotechnol J 2024; 19:e2300473. [PMID: 38528367 DOI: 10.1002/biot.202300473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 03/27/2024]
Abstract
The use of hybrid models is extensively described in the literature to predict the process evolution in cell cultures. These models combine mechanistic and machine learning methods, allowing the prediction of complex process behavior, in the presence of many process variables, without the need to collect a large amount of data. Hybrid models cannot be directly used to predict final product critical quality attributes, or CQAs, because they are usually measured only at the end of the process, and more mechanistic knowledge is needed for many classes of CQAs. The historical models can instead predict the CQAs better; however, they cannot directly relate manipulated process parameters to final CQAs, as they require knowledge of the process evolution. In this work, we propose an innovative modeling approach based on combining a hybrid propagation model with a historical data-driven model, that is, the combined hybrid model, for simultaneous prediction of full process dynamics and CQAs. The performance of the combined hybrid model was evaluated on an industrial dataset and compared to classical black-box models, which directly relate manipulated process parameters to CQAs. The proposed combined hybrid model outperforms the black-box model by 33% on average in predicting the CQAs while requiring only around half of the data for model training to match performance. Thus, in terms of model accuracy and experimental costs, the combined hybrid model in this study provides a promising platform for process optimization applications.
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Affiliation(s)
| | - Zhuangrong Huang
- Biologics Development, Global Product Development and Supply, Bristol Myers Squibb, Devens, Massachusetts, USA
| | | | | | | | - C Eric Hodgman
- Biologics Development, Global Product Development and Supply, Bristol Myers Squibb, Devens, Massachusetts, USA
| | - Michael Borys
- Biologics Development, Global Product Development and Supply, Bristol Myers Squibb, Devens, Massachusetts, USA
| | - Anurag Khetan
- Biologics Development, Global Product Development and Supply, Bristol Myers Squibb, Devens, Massachusetts, USA
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Wen H, Amin MT, Khan F, Ahmed S, Imtiaz S, Pistikopoulos E. Assessment of Situation Awareness Conflict Risk between Human and AI in Process System Operation. Ind Eng Chem Res 2023; 62:4028-4038. [PMID: 38332759 PMCID: PMC10848264 DOI: 10.1021/acs.iecr.2c04310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/09/2023] [Accepted: 02/09/2023] [Indexed: 02/27/2023]
Abstract
The conflict between human and artificial intelligence is a critical issue, which has recently been introduced in Process System Engineering, capturing the observation and action conflicts. Interpretation conflict is another source of potential conflict that can cause serious concern for process safety as it is often perceived as confusion, surprise, or a mistake. It is intangible and associated with situation awareness. However, interpretation conflict has not been studied with the required emphasis. The current work proposes a novel methodology to quantify interpretation conflict probability and risk. The methodology is demonstrated, tested, and validated on a two-phase separator. The results show that interpretation conflict is usually hidden, mixed, or covered by traditional faults, and noises in observation and interpretation, including sensor faults, logic errors, cyberattacks, human mistakes, and misunderstandings, may easily trigger interpretation conflict. The proposed methodology will serve as a mechanism to develop strategies to manage interpretation conflict.
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Affiliation(s)
- He Wen
- Centre
for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering
& Applied ScienceMemorial University, St. John’s, NL A1B 3X5, Canada
| | - Md. Tanjin Amin
- Mary
Kay O’Connor Process Safety Center (MKOPSC), Artie McFerrin
Department of Chemical Engineering, Texas
A&M University, College
Station, Texas 77843, United States
| | - Faisal Khan
- Centre
for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering
& Applied ScienceMemorial University, St. John’s, NL A1B 3X5, Canada
- Mary
Kay O’Connor Process Safety Center (MKOPSC), Artie McFerrin
Department of Chemical Engineering, Texas
A&M University, College
Station, Texas 77843, United States
| | - Salim Ahmed
- Centre
for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering
& Applied ScienceMemorial University, St. John’s, NL A1B 3X5, Canada
| | - Syed Imtiaz
- Centre
for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering
& Applied ScienceMemorial University, St. John’s, NL A1B 3X5, Canada
| | - Efstratios Pistikopoulos
- Texas
A&M Energy Institute, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States
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Bader J, Narayanan H, Arosio P, Leroux JC. Improving extracellular vesicles production through a Bayesian optimization-based experimental design. Eur J Pharm Biopharm 2023; 182:103-114. [PMID: 36526027 DOI: 10.1016/j.ejpb.2022.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 12/05/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022]
Abstract
With the growing demand and diversity of biological drugs, developing optimal processes for their accelerated production with minimal resource utilization is a pressing challenge. Typically, such optimization involves multiple target properties, such as production yield, biological activity, and product purity. Therefore, strategic experimental design techniques that can characterize the parameter space while simultaneously arriving at the optimal process satisfying multiple target properties are required. To achieve this, we propose the use of a multi-objective batch Bayesian optimization (MOBBO) algorithm and illustrate its successful application for the production of extracellular vesicles (EVs) from a 3D culture of mesenchymal stem cells (MSCs) considering three objectives, namely to maximize the vesicle-to-protein ratio, maximize the enzymatic activity of the MSC-EV protein CD73, and minimize the amount of calregulin impurities. We show that the optimal combination of the process parameters to address the intended objectives could be achieved with only 32 experiments. For the four parameters considered (i.e., microcarrier concentration, seeding density, centrifugation time, and impeller speed), this number of experiments is comparable to or lower than the classical design of experiments (DoE) and the traditional one-factor-at-a-time (OFAT) approach. We illustrate how the algorithm adaptively samples in the process parameter space, selectively excluding unfavorable regions, thus minimizing the number of experiments required to reach optimal conditions. Finally, we compare the obtained solutions to the literature data and present possible applications of the collected data for other modeling activities such as Quality by Design, process monitoring, control, and scale-up.
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Affiliation(s)
- Johannes Bader
- Institute of Pharmaceutical Sciences, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | - Harini Narayanan
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | - Paolo Arosio
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland.
| | - Jean-Christophe Leroux
- Institute of Pharmaceutical Sciences, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland.
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Narayanan H, Luna M, Sokolov M, Butté A, Morbidelli M. Hybrid Models Based on Machine Learning and an Increasing Degree of Process Knowledge: Application to Cell Culture Processes. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c04507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Harini Narayanan
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | - Martin Luna
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | | | | | - Massimo Morbidelli
- DataHow AG, Zürichstrasse 137, 8600 Dübendorf, Switzerland
- Dipartimento di Chimica, Materiali e Ingegneria Chimica, Giulio Natta, Politecnico di Milano, 20131 Milano, Italy
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Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design. Processes (Basel) 2022. [DOI: 10.3390/pr10050883] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Development and optimization of biopharmaceutical production processes with cell cultures is cost- and time-consuming and often performed rather empirically. Efficient optimization of multiple objectives such as process time, viable cell density, number of operating steps & cultivation scales, required medium, amount of product as well as product quality depicts a promising approach. This contribution presents a workflow which couples uncertainty-based upstream simulation and Bayes optimization using Gaussian processes. Its application is demonstrated in a simulation case study for a relevant industrial task in process development, the design of a robust cell culture expansion process (seed train), meaning that despite uncertainties and variabilities concerning cell growth, low variations of viable cell density during the seed train are obtained. Compared to a non-optimized reference seed train, the optimized process showed much lower deviation rates regarding viable cell densities (<10% instead of 41.7%) using five or four shake flask scales and seed train duration could be reduced by 56 h from 576 h to 520 h. Overall, it is shown that applying Bayes optimization allows for optimization of a multi-objective optimization function with several optimizable input variables and under a considerable amount of constraints with a low computational effort. This approach provides the potential to be used in the form of a decision tool, e.g., for the choice of an optimal and robust seed train design or for further optimization tasks within process development.
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Holistic Process Models: A Bayesian Predictive Ensemble Method for Single and Coupled Unit Operation Models. Processes (Basel) 2022. [DOI: 10.3390/pr10040662] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
The coupling of individual models in terms of end-to-end calculations for unit operations in manufacturing processes is a challenging task. We present a probability distribution-based approach for the combined outcomes of parametric and non-parametric models. With this so-called Bayesian predictive ensemble, the statistical moments such as mean value and standard deviation can be accurately computed without any further approximation. It is shown that the ensemble of different model predictions leads to an uninformed prior distribution, which can be transformed into a predictive posterior distribution using Bayesian inference and numerical Markov Chain Monte Carlo calculations. We demonstrate the advantages of our method using several numerical examples. Our approach is not restricted to certain unit operations, and can also be used for the more robust interpretation and assessment of model predictions in general.
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Narayanan H, Sponchioni M, Morbidelli M. Integration and digitalization in the manufacturing of therapeutic proteins. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2021.117159] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Hole G, Hole AS, McFalone-Shaw I. Digitalization in pharmaceutical industry: What to focus on under the digital implementation process? Int J Pharm X 2021; 3:100095. [PMID: 34712948 PMCID: PMC8528719 DOI: 10.1016/j.ijpx.2021.100095] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 09/17/2021] [Accepted: 09/21/2021] [Indexed: 11/22/2022] Open
Abstract
Digitalization of any manufacture industry is a key step in any progress of the production process. The process of digitalization includes both increased use of robotics, automatization solutions and computerization, thereby allowing to reduce costs, to improve efficiency and productivity, and to be flexible to changes. Pharmaceutical Industry (PI) has however been resistant to digitalization, mainly due to fair experience and complexity of the entailed development and manufacture processes. Nevertheless, there is a clear need to digitalize PI as the demand in both traditional and new drugs is constantly growing. Contract Development Manufacture Organizations (CDMOs) have a special digitalizing challenge. Digitalization of PI, and CDMO precisely, should be tightly related to the main aspects of Good Manufacture Practice (GMP), and, to succeed in PI digitalizing requires constant focus on GMP. Close collaboration with constantly changing stakeholders is another important factor which should be in focus during digitalization of CDMO. This paper represents an overview over the main aspects of CDMO digitalization and discusses both the opportunities and challenges of the process, focusing on the practical solutions for successive digital implementation.
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Key Words
- AIDS, Acquired Immune Deficiency Syndrome
- CDMO, Contract Development and Manufacturing Organization
- Contract development manufacture organization
- Digitalization
- EMA, European Medicines Agency
- EU, European Union
- FDA, Food and Drug Administration
- GMP, Good Manufacturing Practice
- ITA., International Trade Administration
- MHRA, Medicines and Healthcare Products Regulatory Agency
- PAI, Pre-Approval Inspections
- PI, Pharmaceutical Industry
- Pharmaceutical industry
- Process improvements
- TDM, Traditional Drug Manufacturing
- USD, United States Dollars
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Affiliation(s)
- Glenn Hole
- Molde University College, Molde and Procuratio Consulting, Drammen, Norway
| | | | - Ian McFalone-Shaw
- Molde University College, Molde and Procuratio Consulting, Drammen, Norway
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