Optimization of CAR T-cell therapies supply chains

被引:20
|
作者
Karakostas, Panagiotis [1 ]
Panoskaltsis, Nicki [2 ]
Mantalaris, Athanasios [3 ]
Georgiadis, Michael C. [1 ]
机构
[1] Aristotle Univ Thessaloniki, Dept Chem Engn, Univ Campus, Thessaloniki 54124, Greece
[2] Emory Univ, Sch Med, Dept Hematol & Med Oncol, Atlanta, GA 30332 USA
[3] Georgia Inst Technol, Dept Biomed Engn, Atlanta, GA 30332 USA
关键词
CAR T-Cell therapies; Supply chain optimization; Healthcare supply chains; Metaheuristic techniques; HEALTH-CARE; NETWORK DESIGN; MODEL; SEARCH;
D O I
10.1016/j.compchemeng.2020.106913
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This work presents a new modelling framework and an efficient solution approach for the optimization of supply chain for CAR T-cell therapies. The proposed supply chain structure is patient-centric, as the administration of CAR T-cell therapies is performed in local treatment facilities located close to patients' sites. Cells are re-engineered in a set of available manufacturing centres, the selection of which is to be decided. A limited number of specialized hospitals operate as coordinators of the overall therapy pro-cess. Mobile medical units are considered for delivering therapies from manufacturing centres to local treatment facilities. The underlying problem is formulated as a mixed-integer linear programming model. However, due to the computational complexity, solutions using state-of-the-art solvers such as CPLEX, are obtained only for small and rather unrealistic problem cases. To tackle more realistic-sized problem cases, a General Variable Neighborhood Search (GVNS) algorithm is proposed. The algorithm is tested on 20 new, randomly generated, large problem instances. Solutions were compared with those obtained by CPLEX. Finally, extensive numerical analyses were performed to derive useful insights for the key factors affecting design and operation of CAR T-cell therapies supply chains. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数:16
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