59 results found
Triantafyllou N, Sarkis M, Krassakopoulou A, et al., 2023, Uncertainty quantification for gene delivery methods: A roadmap for pDNA manufacturing from phase I clinical trials to commercialization., Biotechnol J
The fast-growing interest in cell and gene therapy (C>) products has led to a growing demand for the production of plasmid DNA (pDNA) and viral vectors for clinical and commercial use. Manufacturers, regulators, and suppliers need to develop strategies for establishing robust and agile supply chains in the otherwise empirical field of C>. A model-based methodology that has great potential to support the wider adoption of C> is presented, by ensuring efficient timelines, scalability, and cost-effectiveness in the production of key raw materials. Specifically, key process and economic parameters are identified for (1) the production of pDNA for the forward-looking scenario of non-viral-based Chimeric Antigen Receptor (CAR) T-cell therapies from clinical (200 doses) to commercial (40,000 doses) scale and (2) the commercial (40,000 doses) production of pDNA and lentiviral vectors for the current state-of-the-art viral vector-based CAR T-cell therapies. By applying a systematic global sensitivity analysis, we quantify uncertainty in the manufacturing process and apportion it to key process and economic parameters, highlighting cost drivers and limitations that steer decision-making. The results underline the cost-efficiency and operational flexibility of non-viral-based therapies in the overall C> supply chain, as well as the importance of economies-of-scale in the production of pDNA.
Sachio S, Kontoravdi C, Papathanasiou MM, 2023, A model-based approach towards accelerated process development: A case study on chromatography, Chemical Engineering Research and Design, Vol: 197, Pages: 800-820, ISSN: 0263-8762
Process development is typically associated with lengthy wet-lab experiments for the identification of good candidate setups and operating conditions. In this paper, we present the key features of a model-based approach for the identification and assessment of process design space (DSp), integrating the analysis of process performance and flexibility. The presented approach comprises three main steps: (1) model development & problem formulation, (2) DSp identification, and (3) DSp analysis. We demonstrate how such an approach can be used for the identification of acceptable operating spaces that enable the assessment of different operating points and quantification of process flexibility. The above steps are demonstrated on Protein A chromatographic purification of antibody-based therapeutics used in biopharmaceutical manufacturing.
Sarkis M, Shah N, Papathanasiou MM, 2023, Characterization of key manufacturing uncertainties in next generation therapeutics and vaccines across scales, Journal of Advanced Manufacturing and Processing, Vol: 5, ISSN: 2637-403X
<jats:title>Abstract</jats:title><jats:p>Viral vectors are advanced therapy products used as genetic information carriers in vaccine and cell therapy development and manufacturing. Despite the first product receiving market authorization in 2012, viral vector manufacturing has still not reached the level of maturity of biologics and is still highly susceptible to process uncertainties, such as viral titers and chromatography yields. This was exacerbated by the COVID‐19 pandemic when viral vector manufacturers were challenged to respond to the global demand in a timely manner. A key reason for this was the lack of a systematic framework and approach to support capacity planning under uncertainty. To address this, we present a methodology for: (i) identification of process cost and volume bottlenecks, (ii) quantification of process uncertainties and their impact on target key performance indicators, and (iii) quantitative analysis of scale‐dependent uncertainties. We use global sensitivity analysis as the backbone to evaluate three industrially relevant vector platforms: adeno‐associated, lentiviral, and adenoviral vectors. For the first time, we quantify how operating parameters can affect process performance and, critically, the trade‐offs among them. Results indicate a strong, direct proportional correlation between volumetric scales and propagation of uncertainties, while we identify viral titer as the most critical scale‐up bottleneck across the three platforms. The framework can de‐risk investment decisions, primarily related to scale‐up and provides a basis for proactive decision‐making in manufacturing and distribution of advanced therapeutics.</jats:p>
Sadeek S, Chakrabarti D, Papathanasiou MM, et al., 2023, Optimizing the sustainable energy transition: A case study on Trinidad and Tobago, CHEMICAL ENGINEERING RESEARCH & DESIGN, Vol: 192, Pages: 194-207, ISSN: 0263-8762
Sadeek S, Chakrabarti D, Papathanasiou MM, et al., 2023, Optimizing the Sustainable Energy Transition: A Case Study on Trinidad and Tobago, Computer Aided Chemical Engineering, Pages: 2903-2908
Trinidad and Tobago is one of the largest emitters of CO2 per capita globally, with a significant reliance on oil and gas sectors. With the country's commitment, as a small island developing state (SIDS), to sustainable development goals and climate change agreements, rapid redesign of Trinidad and Tobago's power sector is critical to promoting a sustainable energy transition. Hence this study implements a mixed-integer linear programming model (MILP) to assess the levelized cost of electricity (LCOE) and life-cycle greenhouse gas emissions (GHGLC) across five scenarios. The results illustrate that with an improvement in power generation technology and resource efficiency, reduction of LCOE and GHGLC of up to 40% and 24% respectively are possible. For 2030, our results indicate an estimated increase of 29% and 5% for LCOE and GHGLC, respectively compared to the equivalent scenarios in 2019. Ultimately, through a multi-objective optimisation framework, our results highlight the value of systems-based planning and implementation in the sustainable energy transition across the Caribbean region, in accordance with sustainable development goals (SDGs).
Sachio S, Kontoravdi C, Papathanasiou MM, 2023, Embedding Operating Flexibility in Process Design, Computer Aided Chemical Engineering, Pages: 1999-2004
Inherently flexible processes are able to handle disturbances better than point-specific designed processes. However, there is a lack of systematic framework to embed operating flexibility in process design. In this work, we propose a model-based framework for integrated analysis of process flexibility and performance based on the identification and assessment of process design spaces. The proposed framework enables the identification of operating regions, quantification, within which the product and process meet the specifications. The operational flexibility and sensitivity of the process to design and operating parameters is also quantified. We demonstrate the capabilities of the framework on a Protein A chromatographic separation, used in biopharmaceutical manufacturing. We identify the feed flow rate as the most influential process parameter, while we quantify an acceptable range for the feed stream variability (concentration: 0.37 – 0.43 mg ml-1 and flowrate: 0.72 – 0.88 ml min-1) for a fixed process design.
Triantafyllou N, Papaiakovou S, Bernardi A, et al., 2023, Machine learning-based decomposition for complex supply chains, Computer Aided Chemical Engineering, Pages: 1655-1660
Personalised medicine products represent a novel category of therapeutics often characterised by bespoke manufacturing lines and dedicated distribution nodes. An example of such products is Chimeric Antigen Receptor (CAR) T-cells, whose manufacturing poses challenges to volumetric scale-up, leading to increased production and supply chain costs. From a modelling perspective, such networks lead to complex large-scale supply chain models that grow exponentially as the demand increases and more therapies are tracked simultaneously throughout the supply chain. In this work, we present a hybrid model that utilizes the potential of machine learning for strategic planning by forecasting optimal supply chain structures and Mixed Integer Linear Programming (MILP) for detailed scheduling. The proposed model is robust to uncertain demand patterns and can reduce the number of linear constraints and binary variables in the original MILP by more than 64.7%.
Triantafyllou N, Shah N, Papathanasiou MM, et al., 2023, Combined Bayesian optimization and global sensitivity analysis for the optimization of simulation-based pharmaceutical processes, Computer Aided Chemical Engineering, Pages: 381-386
We propose an efficient framework that employs Bayesian optimization and global sensitivity analysis for the optimization of detailed pharmaceutical flowsheets. Global sensitivity analysis based on quasi-random sampling is utilized to reduce the dimensionality of the problem by identifying critical process and economic parameters that contribute significantly to the variability of Key Performance Indicators (KPIs) such as batch size and OpEx. Then, Bayesian optimization is performed in the previously identified critical input space based on gaussian process surrogate models and a number of different acquisition functions to find the optimal critical operating conditions that minimize the aforementioned KPIs. We apply this framework to the manufacture of plasmid DNA (pDNA), which is a critical raw material for advanced therapeutics, leading to a surge in demand for pDNA for clinical or commercial use. Optimized manufacturing recipes identified with the proposed framework are projected to achieve an up to 170% increase in the batch size and a 34.7% decrease in the OpEx per batch.
Sarkis M, Fung J, Lee MH, et al., 2023, Integrating environmental sustainability in next-generation biopharmaceutical supply chains, Computer Aided Chemical Engineering, Pages: 3405-3410
Maximizing product availability to the public and minimizing costs are primary objectives in the biopharmaceutical sector. Nevertheless, awareness of the environmental sustainability of supply chain operations is becoming increasingly relevant in recent years. To assist decision-makers in balancing financial and environmental sustainability we present an optimization framework which determines candidate supply chain structures network designs and operational plans. Supply chain structures are assessed with respect to total cost and environmental score, with the latter integrating environmental impacts related to climate change, water usage and energy consumption. A Pareto set of candidate solutions is found which provides insights in complex trade-offs between impact categories and cost: centralized manufacturing is selected to lower unit production cost and better use water resources, whilst decentralized manufacturing improves energy usage. Emissions from CO2 are lowered through cost minimization.
Triantafyllou N, Bernardi A, Lakelin M, et al., 2022, A digital platform for the design of patient-centric supply chains, Scientific Reports, Vol: 12, ISSN: 2045-2322
Chimeric Antigen Receptor (CAR) T cell therapies have received increasing attention, showing promising results in the treatment of acute lymphoblastic leukaemia and aggressive B cell lymphoma. Unlike typical cancer treatments, autologous CAR T cell therapies are patient-specific; this makes them a unique therapeutic to manufacture and distribute. In this work, we focus on the development of a computer modelling tool to assist the design and assessment of supply chain structures that can reliably and cost-efficiently deliver autologous CAR T cell therapies. We focus on four demand scales (200, 500, 1000 and 2000 patients annually) and we assess the tool’s capabilities with respect to the design of responsive supply chain candidate solutions while minimising cost.
Sachio S, Mowbray M, Papathanasiou MM, et al., 2022, Integrating process design and control using reinforcement learning, Chemical Engineering Research and Design, Vol: 183, Pages: 160-169, ISSN: 0263-8762
To create efficient-high performing processes, one must find an optimal design with its corresponding controller that ensures optimal operation in the presence of uncertainty. When comparing different process designs, for the comparison to be meaningful, each design must involve its optimal operation. Therefore, to optimize a process’ design, one must address design and control simultaneously. For this, one can formulate a bilevel optimization problem, with the design as the outer problem in the form of a mixed-integer nonlinear program (MINLP) and a stochastic optimal control as the inner problem. This is intractable by most approaches. In this paper we propose to compute the optimal control using reinforcement learning, and then embed this controller into the design problem. This allows to decouple the solution procedure, while having the same optimal result as if solving the bilevel problem. The approach is tested in two case studies and the performance of the controller is evaluated. The case studies indicate that the proposed approach outperforms current state-of-the-art simultaneous design and control strategies. This opens a new avenue to address simultaneous design and control of engineering systems.
Sachio S, Mowbray M, Papathanasiou MM, et al., 2022, Integrating process design and control using reinforcement learning, CHEMICAL ENGINEERING RESEARCH & DESIGN, Vol: 183, Pages: 160-169, ISSN: 0263-8762
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.
Ibrahim D, Kis Z, Tak K, et al., 2022, Optimal design and planning of supply chains for viral vectors and RNA vaccines, Computer Aided Chemical Engineering, Pages: 1633-1638
This work develops a multi-product MILP vaccine supply chain model that supports planning, distribution, and administration of viral vectors and RNA-based vaccines. The capability of the proposed vaccine supply chain model is illustrated using a real-world case study on vaccination against SARS-CoV-2 in the UK that concerns both viral vectors (e.g., AZD1222 developed by Oxford-AstraZeneca) and RNA-based vaccine (e.g., BNT162b2 developed by Pfizer-BioNTech). A comparison is made between the resources required and logistics costs when viral vectors and RNA vaccines are used during the SARS-CoV-2 vaccination campaign. Analysis of results shows that the logistics cost of RNA vaccines is 85% greater than that of viral vectors, and that transportation cost dominates logistics cost of RNA vaccines compared to viral vectors.
Sarkis M, Tak K, Chachuat B, et al., 2022, Towards Resilience in Next-Generation Vaccines and Therapeutics Supply Chains, Computer Aided Chemical Engineering, Pages: 931-936
Recent clinical outcomes of Advanced Therapy Medicinal Products (ATMPs) highlight promising opportunities in the prevention and cure of life threatening diseases. ATMP manufacturers are asked to tackle engineering product and process-related challenges, whilst scaling up production under demand uncertainty; this highlights the need for tools supporting supply chain planning under uncertainty. This study presents a computer-aided modelling and optimisation framework for viral vector supply chains. A methodology for the characterisation of process-related uncertainties is presented; the impact of input demand and process bottlenecks on optimal supply chain configurations and capacity allocations is assessed. A trade-off between cost and scalability emerges, larger costs incurring at higher input demands, whilst ensuring improved flexibility under demand uncertainty. Furthermore, bottlenecks uncertainty drives the optimisation to alternative strategic decisions, highlighting the need for a systematic integration within the framework.
Sachio S, Kontoravdi C, Papathanasiou MM, 2022, Model-Based Design Space for Protein A Chromatography Resin Selection, Computer Aided Chemical Engineering, Pages: 733-738
As demand for biopharmaceuticals rises, manufacturers are required to meet multiple competing key performance indicators (KPIs) such as process sustainability, efficiency and product efficacy and quality. Advanced process optimisation and control in biopharmaceutical manufacturing is challenged by the lack of online Process Analytical Technologies (PAT). This results in processes relying heavily on wet-lab experimentation, which may be costly and inefficient. In this work, a novel methodology for evaluating process robustness and alternative operating strategies using design space identification is proposed to accelerate process design and optimisation. The focus in this work is on the initial separation step for the purification of monoclonal antibodies (mAbs) separating the majority of process impurities generated upstream using affinity (protein A) chromatography. A high fidelity process model is used to computationally explore the multidimensional design space. The performance and robustness of the process under three different resin properties and a variety of input conditions are evaluated using the framework. Three scenarios for each of the resins are considered resulting in a total of nine design spaces. The results indicate that using a higher column protein A density resin can increase operational flexibility.
Kis Z, Tak K, Ibrahim D, et al., 2022, Quality by design and techno-economic modelling of RNA vaccine production for pandemic-response, Computer Aided Chemical Engineering, Pages: 2167-2172
Vaccine production platform technologies have played a crucial role in rapidly developing and manufacturing vaccines during the COVID-19 pandemic. The role of disease agnostic platform technologies, such as the adenovirus-vectored (AVV), messenger RNA (mRNA), and the newer self-amplifying RNA (saRNA) vaccine platforms is expected to further increase in the future. Here we present modelling tools that can be used to aid the rapid development and mass-production of vaccines produced with these platform technologies. The impact of key design and operational uncertainties on the productivity and cost performance of these vaccine platforms is evaluated using techno-economic modelling and variance-based global sensitivity analysis. Furthermore, the use of the quality by digital design framework and techno-economic modelling for supporting the rapid development and improving the performance of these vaccine production technologies is also illustrated.
Triantafyllou N, Bernardi A, Lakelin M, et al., 2022, A bi-level decomposition approach for CAR-T cell therapies supply chain optimisation, Computer Aided Chemical Engineering, Pages: 2197-2202
Autologous cell therapies are based on bespoke, patient-specific manufacturing lines and distribution channels. They present a novel category of therapies with unique features that impose scale out approaches. Chimeric Antigen Receptor (CAR) T cells are an example of such products, the manufacturing of which is based on the patient's own cells. This automatically: (a) creates dependencies between the patient and the supply chain schedules and (b) increases the associated costs, as manufacturing lines and distribution nodes are exclusive to the production and delivery of a single therapy. The lack of scale up opportunities and the tight return times required, dictate the design of agile and responsive distribution networks that are eco-efficient. From a modelling perspective, such networks are described by a large number of variables and equations, rendering the problem intractable. In this work, we present a bi-level decomposition algorithm as means to reduce the computational complexity of the original Mixed Integer Linear Programming (MILP) model. Optimal solutions for the structure and operation of the supply chain network are obtained for demands of up to 5000 therapies per year, in which case the original model contains 68 million constraints and 16 million discrete variables.
Triantafyllou N, Bernardi A, Lakelin M, et al., 2022, Fresh vs frozen: assessing the impact of cryopreservation in personalised medicine, Computer Aided Chemical Engineering, Pages: 955-960
Chimeric Antigen Receptor (CAR) T cell therapy is a type of patient-specific cell immunotherapy demonstrating promising results in the treatment of aggressive haematological malignancies. Autologous CAR T cell therapies are based on bespoke manufacturing lines and distribution nodes that are exclusive to the production and delivery of a single therapy. Given their patient-specific nature, they follow a 1:1 business model that challenges volumetric scale up, leading to increased manufacturing and distribution costs. Manufacturers aim to guarantee the in-time delivery and identify ways to reduce the production cost with the ultimate objective of releasing these innovative therapies to a bigger portion of the population. In this work, we investigate upstream storage to the supply chain network as means to introduce greater flexibility in the modus operandi. We formulate and assess different supply chain networks via a Mixed Integer Linear Programming model.
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.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.