•Model-based frameworks for accelerated process development.•Computer-aided resin screening.•Machine learning enhanced dataset generation.•Consideration of process flexibility in resin evaluation.
...The rapidly growing market of monoclonal antibodies (mAbs) within the biopharmaceutical industry has incentivised numerous works on the design of more efficient production processes. Protein A affinity chromatography is regarded as one of the best processes for the capture of mAbs. Although the screening of Protein A resins has been previously examined, process flexibility has not been considered to date. Examining performance alongside flexibility is crucial for the design of processes that can handle disturbances arising from the feed stream. In this work, we present a model-based approach for the identification of design spaces, enhanced by machine learning. We demonstrate its capabilities on the design of a Protein A chromatography unit, screening five industrially relevant resins. The computational results favourably compare to experimental data and a resin performance comparison is presented. An improvement on the computational time by a factor of 300,000 is achieved using the machine learning aided methodology. This allowed for the identification of 5,120 different design spaces in only 19 h.
In this paper we present the main foundations and features of an integrated framework and software platform that enables the use of model-based tools in design, operational optimisation and advanced ...control studies. A step-wise procedure is outlined involving (i) the development of a high-fidelity dynamic model, and its validation and model analysis, (ii) a model approximation step, including system identification, model reduction and global sensitivity analysis, (iii) a receding horizon modelling step for model-predictive control (MPC) and reactive scheduling, (iv) a suite of multi-parametric programming techniques for optimisation under uncertainty, explicit/multi-parametric MPC and state-estimation and (v) an ‘in-silico’ validation step for the derived optimisation, control and/or scheduling strategies to be analysed within the original high-fidelity model. The proposed software platform, PAROC, is also introduced and demonstrated in three different classes of process systems engineering applications; a combined heat and power energy system, a distillation column and a periodic purification process for biopharmaceuticals.
•Framework and software platform for design, operational optimisation and control.•Multiparametric programming and explicit model predictive control frame-work.•Model approximation and moving horizon estimation techniques.•Combined heat and power energy system application.•Biopharmaceutical periodic separation system application.
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.
•Mixed Integer Linear Programming formulation to describe current supply chain networks in personalised medicine.•Assessment of storage as means to increase network flexibility.•Assessment of therapy ...manufacturing time on cost and responsiveness of the network.
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.
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.
Graphical and Lay Summary
Accelerating the production of cell and gene therapies requires an industrial understanding of the different strategies available to address raw material shortages, delays and disruptions, but also identification of cost‐effective solutions. This article assesses the cost‐effectiveness and supply chain resilience of two delivery modes: commercially established viral gene delivery and non‐viral gene delivery for CAR T‐cell therapy manufacturing, a potential future alternative. The analysis highlights the benefits and drawbacks of in‐house versus outsourcing and centralized versus decentralized manufacturing not only from the perspective of cell and gene therapy manufacturing but also considering raw materials acquisition with an emphasis on DNA. The assessment considers efficient timelines and scalability of the pDNA production process, while still maintaining reasonable costs.
•Design and validation of a control scheme for the semi-continuous MCSGP separation process, based on mp-MPC techniques.•Use of the integral concentrations as outputs in the control problem, creating ...a solid basis for continuous control throughout the process cycle, enabling easier online measurements.•The control scheme inherently suggests a periodic input profile that could allow cyclic steady state to be achieved.•Good agreement between the computational (control) results and the experimentally optimized profiles as provided by ETHZ.
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.
Left ventricular assist devices (LVAD) are increasingly implanted in advanced heart failure patients to improve survival and quality of life either as a bridge to transplant, bridge to recovery or as ...destination therapy. LVAD therapy is often accompanied by a profound lowering of pulmonary artery pressure due to mechanical unloading of the left ventricle. Persistent pulmonary hypertension (PH) after LVAD implantation increases the risk of right ventricular failure (RVF). In this context pulmonary vasodilators have been implemented: a) as a strategy to reduce afterload and wean patients with RVF from inotropes in the early postoperative period, b) as long-term therapy aiming to optimize right heart hemodynamics and prevent late RVF and c) in order to lower persistently elevated pulmonary artery pressure (PAP) and pulmonary vascular resistance (PVR) after LVAD and enable candidacy for heart transplantation. However, considerable uncertainty exists regarding the risks and benefits of these strategies and practices vary widely among institutions. This article provides an overview of the available evidence and existing recommendations regarding the use of pulmonary vasodilators in LVAD recipients.
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.
This work presents a computer‐aided framework for the characterization and quantification of scale‐dependent uncertainty in process productivity and costs, which drive capacity planning for advanced therapeutics and vaccines.
•Comprehensive review of the developments in multi-parametric programming over the last 20 years.•Detailed description of applications beyond explicit model predictive control.•Opinionated view of ...future developments within multi-parametric programming and its applications.
In multi-parametric programming, an optimization problem is solved for a range and as a function of multiple parameters. In this review, we discuss the main developments of multi-parametric programming over the last two decades from a theoretical, algorithmic and application perspective. In addition, we provide an opinionated view of the future research directions in multi-parametric programming.
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