Supply Chain Optimization of CAR-T Cell Therapies

Δουφεξή, Γωργία; Doufexi, Gorgia

dc.contributor.author	Δουφεξή, Γωργία	el
dc.contributor.author	Doufexi, Gorgia	en
dc.date.accessioned	2022-12-08T08:58:44Z
dc.date.available	2022-12-08T08:58:44Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/56372
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.24070
dc.rights	Default License
dc.subject	Εφοδιαστική Αλυσίδα	el
dc.subject	Γραμμικός προγραμματισμός	el
dc.subject	Κυτταρικές θεραπείες	el
dc.subject	Πολυκριτηριακή βελτιστοποίηση	el
dc.subject	Μαθηματική μοντελοποίηση	el
dc.subject	CAR-T cell therapy	en
dc.subject	Mixted Integer Linear Programming	en
dc.subject	Supply Chain Management	en
dc.subject	Mathematical modeling	en
dc.subject	Multi-objective optimization	en
dc.title	Supply Chain Optimization of CAR-T Cell Therapies	en
heal.type	bachelorThesis
heal.classification	Supply Chain Optimization	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2022-07-07
heal.abstract	Personalized Cell Therapies form a novel class of biologic therapeutics which pave the way to treatment of life-threatening diseases, such as cancer. CAR-T cells are currently at the forefront of cell therapies targeting blood cancers and there already are FDA approved therapies being used in a small number of patients; the demand for CAR-T cell therapies is continuously increasing and manufacturers must tackle difficulties concerning the engineering of product and the production process, while scaling up their production. This highlights the need for sophisticated decision-making tools, which enable effective manufacturing and distribution planning throughout product lifetimes. Process systems engineering (PSE) has traditionally assisted the pharmaceutical industry in the development of such tools. CAR-T cell therapies at present are very expensive due to the following reasons: (1) they are autologous therapies and raw materials are different for each therapy, (2) they have increased logistic costs due to the need of special handling during transportation and because manufacturing sites are not always close to hospitals (3) capacity of existing manufacturing facilities is limited. As a result, the optimization of their supply chain using a MILP model can indicate optimal network structures that can be established and will enable up-scaling of their production while reducing manufacturing and logistics costs. Computational challenges also emerge due to the demand uncertainty that characterizes this new industry that is still developing. Subsequently, the development of robust supply chain networks able to absorb shocks in the demand is imperative. A series of research objectives is proposed, followed by the design of different MILP models for each of them and the presentation of relevant results. The three focus areas of this thesis are: multi-objective optimization for an optimal network determination, demand maximization and supply chain robustness when network is fixed and optimal allocation of patients in the leukapheresis site is allowed and introduction of waiting time to an otherwise ideal supply chain without delays. The design focuses on the UK CAR-T cell therapies supply chain and evaluates three different demand levels and network sizes. This thesis is conducted in the Industrial Process Systems Engineering Unit (IPSEN) of NTUA directed by Professor Kokossis and in collaboration with Professor Papathanasiou from Centre for Process Systems Engineering (CPSE) of Imperial College London. The project is conducted under the umbrella of the UK Engineering & Physical Sciences Research Council (EPSRC) for the Future Targeted Healthcare Manufacturing Hub.	en
heal.advisorName	Κοκόσης, Αντώνης	el
heal.committeeMemberName	Σαρίμβεης, Χαράλαμπος	el
heal.committeeMemberName	Βουγιούκα, Σταματίνα	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Χημικών Μηχανικών. Τομέας Ανάλυσης, Σχεδιασμού και Ανάπτυξης Διεργασιών και Συστημάτων (ΙΙ)	el
heal.academicPublisherID	ntua
heal.numberOfPages	111 σ.	el
heal.fullTextAvailability	false