(494a) Multi-Objective Supply Chain Optimization in Personalized Healthcare
AIChE Annual Meeting
Wednesday, November 10, 2021 - 12:30pm to 12:53pm
Unlike batch produced pharmaceuticals, CAR T cell therapies use patient cells as the starting material, placing the patient schedule in the center of the decision-making process. This unique 1-to-1 production and supply chain model translates into manufacturing lines and distribution nodes designed for and occupied by a single therapy, posing hurdles to the development of robust and responsive networks. Moreover, the predicted increase of patient numbers, the sensitive nature and short shelf-life of these therapeutics pose additional challenges to the coordination of manufacturing, storage and distribution .
In this work, we present a Mixed Integer Linear Programming (MILP) model for the optimal design of a supply chain network of CAR T cell therapies under different demand scenarios. The problem is formulated as multi-objective optimization aiming to minimize the average therapy cost (ATC) and the average return time of the therapies (ART). The preliminary results showcased in Figure 1 highlight the trade-off between the two objectives. The results are reported for two annual demand profiles of 200 (triangles) and 500 (squares) patients and two different manufacturing durations (TMFE). The base case scenario represents the current state of the art of 13 days of manufacturing (filled symbols), while a forward-looking scenario where the manufacturing time is reduced to 7 days is also considered (empty symbols).
We can observe that in the base case scenario an increase in the demand leads to an increase of the average therapy cost for the same ATR, which indicates that for prolonged manufacturing time the effect of economies of scale is not significant. On the other hand, in the forward-looking scenario it is observed that for long manufacturing times an increased demand may lead to facilities with capacity in order to satisfy all patients in time.
Future directions include: (i) investigation of scenarios with higher number of patients per year; and (ii) assessment of the effect of patient-specific manufacturing time on the supply chain network. The second point is particularly important, since the patient conditions at the time the cell sample is taken influence significantly cell doubling time and consequently the total manufacturing time required. The results will help identify bottlenecks in the overall manufacturing and distribution process, as well as the impact of a variable manufacturing time on the overall network. Finally, each scenario is simulated for different demand profiles in order to quantify the sensitivity of the patients scheduling for a given total demand and manufacturing time.
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 organizations is also acknowledged.
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