41 results found
Kis Z, Tak K, Ibrahim D, et al., 2022, Pandemic-response adenoviral vector and RNA vaccine manufacturing, npj Vaccines, Vol: 7, ISSN: 2059-0105
Rapid global COVID-19 pandemic response by mass vaccination is currently limited by the rate of vaccine manufacturing. This study presents a techno-economic feasibility assessment and comparison of three vaccine production platform technologies deployed during the COVID-19 pandemic: (1) adenovirus-vectored (AVV) vaccines, (2) messenger RNA (mRNA) vaccines, and (3) the newer self-amplifying RNA (saRNA) vaccines. Besides assessing the baseline performance of the production process, impact of key design and operational uncertainties on the productivity and cost performance of these vaccine platforms is evaluated using variance-based global sensitivity analysis. Cost and resource requirement projections are computed for manufacturing multi-billion vaccine doses for covering the current global demand shortage and for providing annual booster immunisations. The model-based assessment provides key insights to policymakers and vaccine manufacturers for risk analysis, asset utilisation, directions for future technology improvements and future pidemic/pandemic preparedness, given the disease-agnostic nature of these vaccine production platforms.
Bernardi A, Sarkis M, Triantafyllou N, et al., 2022, Assessment of intermediate storage and distribution nodes in personalised medicine, Computers & Chemical Engineering, Vol: 157, Pages: 107582-107582, ISSN: 0098-1354
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 upstream of the network to: (a) debottleneck manufacturing lines and (b) increase facility utilisation. In this setting, we assess cost-effectiveness and flexibility of the supply chain and we evaluate network performance with respect to: (a) average production cost and (b) average response treatment time. The trade-off between cost-efficiency and responsiveness is examined and discussed.
Ibrahim D, Kis Z, Tak K, et al., 2021, Model-based planning and delivery of mass vaccination campaigns against infectious disease: application to the COVID-19 pandemic in the UK, Vaccines, Vol: 9, Pages: 1-19, ISSN: 2076-393X
Vaccination plays a key role in reducing morbidity and mortality caused by infectious diseases, including the recent COVID-19 pandemic. However, a comprehensive approach that allows the planning of vaccination campaigns and the estimation of the resources required to deliver and administer COVID-19 vaccines is lacking. This work implements a new framework that supports the planning and delivery of vaccination campaigns. Firstly, the framework segments and priorities target populations, then estimates vaccination timeframe and workforce requirements, and lastly predicts logistics costs and facilitates the distribution of vaccines from manufacturing plants to vaccination centres. The outcomes from this study reveal the necessary resources required and their associated costs ahead of a vaccination campaign. Analysis of results shows that by integrating demand stratification, administration, and the supply chain, the synergy amongst these activities can be exploited to allow planning and cost-effective delivery of a vaccination campaign against COVID-19 and demonstrates how to sustain high rates of vaccination in a resource-efficient fashion.
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, Pages: 1-7, 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, Pages: 100689-100689, ISSN: 2211-3398
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
Biologics represent the fastest growing sector of the pharmaceutical industry, yet their manufacture lags significantly behind that of small molecule drugs. This paper discusses the main product-related and process-related challenges during the development and production of therapeutic proteins, with particular focus on product heterogeneity and process monitoring and analytics. Emphasis is placed on novel contributions from the field of computational research that aim to enable the application of model-based process control strategies or are working towards the development of a digital twin of bioprocesses. Lastly, we review promising developments in the paradigm shift from batch to continuous processing.
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
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, 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.
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
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.
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
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
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, 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
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
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
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
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