(442d) Enabling Precision Healthcare through Patient-Centric Supply Chain Networks

Chimeric Antigen Receptor (CAR) T cell therapies are promising to create a step-change in the current standard practice of cancer treatment. With two commercial products in the market [1], [2] CAR T cell therapies are introducing a novel perspective in cancer treatment, using patient cells as the starting material for therapy manufacturing [3]. The latter is placing patient schedule in the center of the decision-making process, thus adding another level of complexity in the coordination of manufacturing and distribution tasks. Further to that, the rising patient population is urging the pharmaceutical industry to identify and design robust strategies to ensure that the manufacturing and supply chain procedure in place can meet the forecasted demand.

Contrary to batch produced pharmaceuticals, CAR T cell therapies are characterized by the unique feature of a 1:1 manufacturing and distribution model. This is translated into manufacturing and supply chain networks designed and occupied for the production of a single therapy. As a result, and due to their autologous nature, scale-up is replaced by scale-out in this case, posing hurdles to the design of robust and responsive networks. Moreover, such therapeutics require sensitive handling during transport and are characterized by short shelf lives that accompany transport and storage decisions [4].

In this work, we showcase a Mixed Integer Linear Programming (MILP) problem developed for the performance assessment of candidate supply chain networks based on a cost-benefit analysis. The suggested models investigate various supply chain configurations of different echelon complexity under demand uncertainty. Additionally, we introduce for the first time the concept of a “dynamic” supply chain network that is versatile and responsive to the increasing demand as manufacturers move from clinical trials to commercialization. The generated solutions are assessed with respect to: (a) cost effectiveness, (b) scalability and (c) responsiveness with respect to patient schedule. Lastly, the trade-off between cost-efficiency and responsiveness is examined and discussed.

Acknowledgments

Funding from the UK Engineering & Physical Sciences Research Council (EPSRC) for the Future Targeted Healthcare Manufacturing Hub hosted at University College London with UK university partners is gratefully acknowledged (Grant Reference: EP/P006485/1). Financial and in-kind support from the consortium of industrial users and sector organisations is also acknowledged.

References

[1] Novartis, “KYMRIAH Treatment Process, Dosing & Administration | HCP,” 2018. [Online]. Available: https://www.hcp.novartis.com/products/kymriah/acute-lymphoblastic-leukem.... [Accessed: 07-Mar-2019].

[2] Kite Pharma, “First CAR T Therapy for Certain Types of Relapsed or Refractory B-Cell Lymphoma,” 2018. [Online]. Available: https://www.yescartahcp.com/. [Accessed: 07-Mar-2019].

[3] B. L. Levine, J. Miskin, K. Wonnacott, and C. Keir, “Global Manufacturing of CAR T Cell Therapy,” Mol. Ther. - Methods Clin. Dev., vol. 4, no. March, pp. 92–101, 2017.

[4] R. Griffiths and M. Lakelin, “Successfully managing the unique demands of cell therapy supply chains.” p. 9, 2017.