(144e) Integrated Supply Chain Network Design and Inventory Management for Autologous Cell Therapy.

Personalized medicine is gaining a lot of attention in the pharmaceutical industry as it can treat diseases that earlier had no cure. In the past few years, we witnessed exponential growth in the development of cell and gene therapy products. Cancer Research Institute reported that 753 cancer cell therapies are currently under development all over the world, of which 375 are in clinical trials [1]. There has been an increase in the growth and demand for autologous therapy due to the recent approval from the FDA [2]. Therefore, autologous therapy is undergoing the transformation from laboratory to commercial scale.

The autologous therapy starts with a collection of cells from the patient, which are cryopreserved and sent to the manufacturing site. The cells are then formulated and enriched. The formulated cells are again cryopreserved and transferred back to the patient, where they are injected back. Unlike traditional pharmaceutical drugs that are produced in bulk, these drugs are patient-centric and have to be manufactured for each patient separately. In addition, the process also deals with living cells that go around different nodes in the supply chain. Therefore, the cells have to be cryopreserved in storage device and transported across the supply chain.

An integrated approach is considered for supply chain network design and inventory management of the raw materials. We consider a network with a set of hospitals where the patients arrive. The facilities to be set up are distribution centers and manufacturing centers. The manufacturing center receives the samples from the patient and engineers the cells to create the drug. The distribution centers have the inventory for the storage containers and are installed for the intermediate halt for the sample transportation between the hospital and manufacturing center. To account for the transit times for the material transfer between facilities, we define the parameter accounting for both transportation time and waiting time for different transportation modes as well as the processing times at each of the node. We also simultaneously optimize inventory policy. We use periodic-review policy for the raw materials inventory at the distribution center [3]. Under this policy, if the inventory is below the defined level, the order is placed to restore the inventory to level . Since the storage devices are reusable, the policy is modified to account for the returning devices after the delivery of the drug.

Given the demand distribution, the potential locations of distribution and manufacturing centers, costs and time for processing, inventory, and transportation, we determine the location of different centers, inventory levels at the distribution centers, response time, and cost of delivery for each sample, with objective function of minimizing the cost and response time. The problem is formulated as a single objective optimization where response time is multiplied by a factor and added to the cost function. A case study of China is considered, with 20 hospitals situated in different provinces and a total of 130 patients over a period of 1 month. Also, the location of 20 potential locations of the distribution centers and 20 manufacturing centers are given. We found out that the majority of the cost is incurred in setting up manufacturing and distribution centers. The cost accounting for the response time depends on the multiplicative factor we choose. In addition, we also explored the importance of batching the samples at the distribution centers and transporting to the manufacturing center subjected to the response time constraint. Decisions related to inventory of raw material are critical to have an efficient supply chain. Also, sensitivity analysis is conducted to study the effect of different parameters on the optimal network design and overall cost.

References:

1. Tang, J., et al., The global landscape of cancer cell therapy. Nat Rev Drug Discov, 2018. 17(7): p. 465-466.
2. Bach, P.B., S.A. Giralt, and L.B. Saltz, FDA approval of tisagenlecleucel: promise and complexities of a $475 000 cancer drug. Jama, 2017. 318(19): p. 1861-1862.
3. Brunaud, B., et al., Inventory policies and safety stock optimization for supply chain planning. AIChE Journal, 2019. 65(1): p. 99-112.