Publications
38 results found
Kotidis P, Pappas I, Avraamidou S, et al., 2021, DigiGlyc: A hybrid tool for reactive scheduling in cell culture systems, Computers and Chemical Engineering, Vol: 154, ISSN: 0098-1354
Chinese hamster ovary (CHO) cell culture systems are the most widely used platform for the industrial production of monoclonal antibodies (mAbs). The optimisation of manufacturing conditions for these high-value products is largely conducted off-line with little or no monitoring of mAb quality in-process. Here, we propose DigiGlyc, a hybrid model of these systems that predicts the critical quality attribute of mAb galactosylation. Having shown that DigiGlyc describes a wide range of experimental data well, we demonstrate that it can be used for the design of reactive optimisation studies. This hybrid formulation offers considerable gains in computational speed compared to mechanistic models with no loss in fidelity and can therefore underpin future online control and optimisation studies.
Sarkis M, Bernardi A, Shah N, et al., 2021, Decision support tools for next-generation vaccines and advanced therapy medicinal products: present and future, Current Opinion in Chemical Engineering, Vol: 32
Advanced Therapy Medicinal Products (ATMPs) are a novel class of biological therapeutics that utilise ground-breaking clinical interventions to prevent and treat life-threatening diseases. At the same time, viral vector-based and RNA-based platforms introduce a new generation of vaccine manufacturing processes. Their clinical success has led to an unprecedented rise in the demand that, for ATMPs, leads to a predicted market size of USD 9.6 billion by 2026. This paper discusses how mathematical models can serve as tools to assist decision-making in development, manufacturing and distribution of these new product classes. Recent contributions in the space of process, techno-economic and supply chain modelling are highlighted. Lastly, we present and discuss how Process Systems Engineering can be further advanced to support commercialisation of advanced therapeutics and vaccines.
Antonakoudis A, Kis Z, Kontoravdi K, et al., 2021, Accelerating product and process development through a model centric approach, Quality by Design - An Indispensable Approach to Accelerate Biopharmaceutical Product Development, Editors: Campa, Khan, Publisher: Parenteral Drug Association, Inc., Pages: 285-338, ISBN: 978-1-945584-22-0
Kis Z, Papathanasiou M, Kotidis P, et al., 2021, Stability modelling for biopharmaceutical process intermediates, Quality by Design - An Indispensable Approach to Accelerate Biopharmaceutical Product Development, Editors: Campa, Khan, Publisher: Parenteral Drug Association, Inc, Pages: 200-225, ISBN: 978-1-945584-22-0
Sarkis M, Bernardi A, Shah N, et al., 2021, Emerging challenges and opportunities in pharmaceutical manufacturing and distribution, Processes, Vol: 9, Pages: 1-16, ISSN: 2227-9717
The rise of personalised and highly complex drug product profiles necessitates significant advancements in pharmaceutical manufacturing and distribution. Efforts to develop more agile, responsive, and reproducible manufacturing processes are being combined with the application of digital tools for seamless communication between process units, plants, and distribution nodes. In this paper, we discuss how novel therapeutics of high-specificity and sensitive nature are reshaping well-established paradigms in the pharmaceutical industry. We present an overview of recent research directions in pharmaceutical manufacturing and supply chain design and operations. We discuss topical challenges and opportunities related to small molecules and biologics, dividing the latter into patient- and non-specific. Lastly, we present the role of process systems engineering in generating decision-making tools to assist manufacturing and distribution strategies in the pharmaceutical sector and ultimately embrace the benefits of digitalised operations.
Bernardi A, Papathanasiou M, Lakelin MW, et al., 2021, Assessment of intermediate storage and distribution nodes in personalised medicine, Computer Aided Chemical Engineering, Pages: 1997-2002
Chimeric Antigen Receptor (CAR)-T cell therapies are a type of patient-specific cell immunotherapy demonstrating promising results in the treatment of aggressive blood cancer types. CAR-T cells follow a 1:1 business model, translating into manufacturing lines and distribution nodes being exclusive to the production of a single therapy, hindering volumetric scale up. In this work, we address manufacturing capacity bottlenecks via a Mixed Integer Linear Programming (MILP) model. The proposed formulation focuses on the design of candidate supply chain network configurations under different demand scenarios. We investigate the effect of an intermediate storage option upstream of the network as means of: (a) debottlenecking manufacturing lines and (b) increasing facility utilisation. In this setting, we assess cost-effectiveness and flexibility of a decentralised supply chain and we evaluate network performance with respect to two key performance indicators (KPIs): (a) average production cost and (b) average response treatment time. The trade-off between cost-efficiency and responsiveness is examined and discussed.
Papathanasiou MM, Stamatis C, Lakelin M, et al., 2020, Autologous CAR T-cell therapies supply chain: challenges and opportunities?, Cancer Gene Therapy, Vol: 27, Pages: 799-809, ISSN: 0929-1903
Chimeric antigen receptor (CAR) T cells are considered a potentially disruptive cancer therapy, showing highly promisingresults. Their recent success and regulatory approval (both in the USA and Europe) are likely to generate a rapidly increasingdemand and a need for the design of robust and scalable manufacturing and distribution models that will ensure timely andcost-effective delivery of the therapy to the patient. However, there are challenging tasks as these therapies are accompaniedby a series of constraints and particularities that need to be taken into consideration in the decision-making process. Here, wepresent an overview of the current state of the art in the CAR T cell market and present novel concepts that can debottleneckkey elements of the current supply chain model and, we believe, help this technology achieve its long-term potential.
Papathanasiou MM, Kontoravdi C, 2020, Engineering challenges in therapeutic protein product and process design, CURRENT OPINION IN CHEMICAL ENGINEERING, Vol: 27, Pages: 81-88, ISSN: 2211-3398
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- Citations: 12
Moschou D, Papathanasiou MM, Lakelin M, et al., 2020, Investment Planning in Personalised Medicine, Editors: Pierucci, Manenti, Bozzano, Manca, Publisher: ELSEVIER SCIENCE BV, Pages: 49-54
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- Citations: 1
Kis Z, Papathanasiou M, CalvoSerrano R, et al., 2019, A model‐based quantification of the impact of new manufacturing technologies on developing country vaccine supply chain performance: A Kenyan case study, Journal of Advanced Manufacturing and Processing, Vol: 1, ISSN: 2637-403X
Papathanasiou MM, Burnak B, Katz J, et al., 2019, Assisting continuous biomanufacturing through advanced control in downstream purification, Computers and Chemical Engineering, Vol: 125, Pages: 232-248, ISSN: 0098-1354
Aiming to significantly improve their processes and secure market share, monoclonal antibody (mAb) manufacturers seek innovative solutions that will yield improved production profiles. In that space, continuous manufacturing has been gaining increasing interest, promising more stable processes with lower operating costs. However, challenges in the operation and control of such processes arise mainly from the lack of appropriate process analytics tools that will provide the required measurements to guarantee product quality. Here we demonstrate a Process Systems Engineering approach for the design a novel control scheme for a semi-continuous purification process. The controllers are designed employing multi-parametric Model Predictive Control (mp-MPC) strategies and the successfully manage to: (a) follow the system periodicity, (b) respond to measured disturbances and (c) result in satisfactory yield and product purity. The proposed strategy is also compared to experimentally optimized profiles, yielding a satisfactory agreement.
Papathanasiou MM, Burnak B, Katz J, et al., 2019, Control of a dual mode separation process via multi-parametric Model Predictive Control, 12th International-Federation-of-Automatic-Control (IFAC) Symposium on Dynamics and Control of Process Systems including Biosystems (DYCOPS), Publisher: ELSEVIER SCIENCE BV, Pages: 988-993, ISSN: 2405-8963
Papathanasiou MM, Burnak B, Katz J, et al., 2019, CONTROL OF SMALL-SCALE CHROMATOGRAPHIC SYSTEMS UNDER DISTURBANCES, Editors: Munoz, Laird, Realff, Publisher: ELSEVIER SCIENCE BV, Pages: 269-274
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- Citations: 4
Papathanasiou MM, Onel M, Nascu I, et al., 2018, Computational tools in the assistance of personalized healthcare, Computer Aided Chemical Engineering, Pages: 139-206, ISBN: 9780444639646
Process Systems Engineering has been many years in the forefront, advancing the standards in healthcare and beyond. Gradually, integrated methods that utilize both experimental and/or clinical data, as well as in silico tools are becoming popular among the medical community. In silico tools have already demonstrated their great potential in various sectors, assisting the industry to produce experiments of significantly reduced cost that allow thorough investigation of the system at hand. Similarly, in biomedical systems, the advancement of the current state of the art through the development of intelligent computational tools can lead to personalized healthcare protocols. The first part of this chapter serves as a brief review of the computational tools commonly used in healthcare, such as big data analytics and dynamic mathematical models. The challenges characterizing biomedical systems, such as data availability and patient variability, are also discussed here. We present the advantages and limitations of the various methods and we suggest a generic framework for the design and testing of advanced in silico platforms. The PARametric Optimization and Control (PAROC) framework presented here is based on the design of high-fidelity, dynamic, mathematical models that are then validated using experimental and/or clinical data. Such models provide the basis for the execution of optimization and control studies for the design of patient-specific treatment protocols. The final part of the chapter is dedicated to the application of PAROC to three different biomedical examples, namely: (i) acute myeloid leukemia, (ii) the anesthesia process, and (iii) diabetes mellitus. The challenges of each case are discussed and the application of the relevant PAROC steps is demonstrated.
Wang X, Kong Q, Papathanasiou MM, et al., 2018, Precision healthcare supply chain design through multi-objective stochastic programming, Computer Aided Chemical Engineering, Pages: 2137-2142
Following the FDA's historic approval of the first cell-based, autologous, cancer therapy in 2017, there has been an increasing growth in the personalized cell therapy market. Both the personalized character as well as the sensitive nature of these therapies, has increased the complexity of their supply chain design and optimisation. In this work, we have addressed key issues in the cyclic supply chain for simultaneous design of the supply chain and the manufacturing plan. A comprehensive optimisation based methodology through both deterministic and stochastic programming is presented and applied to study the Chimeric Antigen Receptor (CAR) T cell therapies. Multiple objectives including maximisation of the overall net present value (NPV) and minimisation of the average response time of all patients are evaluated, while accounting the uncertainties in patients’ demand distribution. Results indicate that the total benefits from the optimized supply chain management are significant compared with the current global market.
Papathanasiou MM, Steinebach F, Morbidelli M, et al., 2017, Intelligent, model-based control towards the intensification of downstream processes, COMPUTERS & CHEMICAL ENGINEERING, Vol: 105, Pages: 173-184, ISSN: 0098-1354
Papathanasiou MM, Quiroga-Campano AL, Steinebach F, et al., 2017, Advanced Model-Based Control Strategies for the Intensification of Upstream and Downstream Processing in mAb Production, BIOTECHNOLOGY PROGRESS, Vol: 33, Pages: 966-988, ISSN: 8756-7938
Oberdieck R, Diangelakis NA, Nascu I, et al., 2016, On multi-parametric programming and its applications in process systems engineering, CHEMICAL ENGINEERING RESEARCH & DESIGN, Vol: 116, Pages: 61-82, ISSN: 0263-8762
Oberdieck R, Diangelakis NA, Papathanasiou MM, et al., 2016, POP - Parametric Optimization Toolbox, INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, Vol: 55, Pages: 8979-8991, ISSN: 0888-5885
Papathanasiou MM, Avraamidou S, Oberdieck R, et al., 2016, Advanced control strategies for the multicolumn countercurrent solvent gradient purification process, AIChE Journal, Vol: 62, Pages: 2341-2357, ISSN: 0001-1541
The multicolumn countercurrent solvent gradient purification process (MCSGP) is a semicontinuous, chromatographic separation process used in the production of monoclonal antibodies) . The process is characterized by high model complexity and periodicity that challenge the development of control strategies, necessary for feasible and efficient operation and essential toward continuous production. A novel approach for the development of control policies for the MCSGP process, which enables efficient continuous process control is presented. Based on a high fidelity model, the recently presented PAROC framework and software platform that allows seamless design and in-silico validation of advanced controllers for complex systems are followed. The controller presented in this work is successfully tested against disturbances and is shown to efficiently capture the process periodic nature.
Papathanasiou MM, Quiroga-Campano AL, Oberdieck R, et al., 2016, Development of advanced computational tools for the intensification of monoclonal antibody production, 26th European Symposium on Computer Aided Process Engineering (ESCAPE), Publisher: ELSEVIER SCIENCE BV, Pages: 1659-1664, ISSN: 1570-7946
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- Citations: 1
Campano AQ, Papathanasiou MM, Pistikopoulos EN, et al., 2016, A Predictive Model for Energy Metabolism and ATP Balance in Mammalian Cells: Towards the Energy-Based Optimization of mAb Production, 26th European Symposium on Computer Aided Process Engineering (ESCAPE), Publisher: ELSEVIER SCIENCE BV, Pages: 1581-1586, ISSN: 1570-7946
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- Citations: 4
Nascu I, Diangelakis NA, Oberdieck R, et al., 2016, Explicit MPC in real-world applications: the PAROC framework, American Control Conference (ACC), Publisher: IEEE, Pages: 913-918, ISSN: 0743-1619
Papathanasiou MM, Oberdieck R, Avraamidou S, et al., 2016, Development of advanced control strategies for periodic systems: An application to chromatographic separation processes, American Control Conference (ACC), Publisher: IEEE, Pages: 4175-4180, ISSN: 0743-1619
Papathanasiou MM, Su M, Oberdieck R, et al., 2016, A centralized/decentralized control approach for periodic systems with application to chromatographic separation processes, 11th IFAC Symposium on Dynamics and Control of Process Systems including Biosystems, Publisher: ELSEVIER SCIENCE BV, Pages: 159-164, ISSN: 2405-8963
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- Citations: 1
Papathanasiou MM, Mantalaris A, Pistikopoulos EN, 2016, Advanced control strategies for a periodic, two-column chromatographic process, IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), Publisher: IEEE, Pages: 387-392, ISSN: 1844-7872
Papathanasioua MM, Oberdieck R, Mantalaris A, et al., 2016, Computational tools for the advanced control of periodic processes - Application to a chromatographic separation, 26th European Symposium on Computer Aided Process Engineering (ESCAPE), Publisher: ELSEVIER SCIENCE BV, Pages: 1665-1670, ISSN: 1570-7946
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- Citations: 1
Pistikopoulos EN, Diangelakis NA, Oberdieck R, et al., 2015, PAROC-An integrated framework and software platform for the optimisation and advanced model-based control of process systems, CHEMICAL ENGINEERING SCIENCE, Vol: 136, Pages: 115-138, ISSN: 0009-2509
Velliou EG, Brito dos Santos SUSANA, Papathanasiou MM, et al., 2015, Towards unravelling the kinetics of an acute myeloid leukaemia model system under oxidative and starvation stress: a comparison between two- and three-dimensional cultures, Bioprocess and Biosystems Engineering: bioreactors, upstream and downstream processes, measurement and control, Vol: 38, Pages: 1589-1600, ISSN: 0178-515X
A great challenge when conducting ex vivo studies of leukaemia is the construction of an appropriate experimental platform that would recapitulate the bone marrow (BM) environment. Such a 3D scaffold system has been previously developed in our group [1]. Additionally to the BM architectural characteristics, parameters such as oxygen and glucose concentration are crucial as their value could differ between patients as well as within the same patient at different stages of treatment, consequently affecting the resistance of leukaemia to chemotherapy. The effect of oxidative and glucose stress—at levels close to human physiologic ones—on the proliferation and metabolic evolution of an AML model system (K-562 cell line) in conventional 2D cultures as well as in 3D scaffolds were studied. We observed that the K-562 cell line can proliferate and remain alive for 2 weeks in medium with glucose close to physiological levels both in 20 and 5 % O2. We report interesting differences on the cellular response to the environmental, i.e., oxidative and/or nutritional stress stimuli in 2D and 3D. Higher adaptation to oxidative stress under non-starving conditions is observed in the 3D system. The glucose level in the medium has more impact on the cellular proliferation in the 3D compared to the 2D system. These differences can be of significant importance both when applying chemotherapy in vitro and also when constructing mathematical tools for optimisation of disease treatment.
Papathanasiou MM, Reineke K, Gogou E, et al., 2015, Impact of high pressure treatment on the available glucose content of various starch types: A case study on wheat, tapioca, potato, corn, waxy corn and resistant starch (RS3), Innovative Food Science & Emerging Technologies, Vol: 30, Pages: 24-30, ISSN: 1466-8564
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