Centralized Management of Pharmaceutical Clinical Trial Supply Chain

        In the new drug development process, clinical trials with different testing objectives (e.g. safety, efficacy, side effects) constitute a critically important and very expensive part as it involves producing, distributing and administering the candidate therapy to an increasing number of volunteer patients located in different geographic zones. Clinical trial supply chain management problem is characterized by a sequence of complicated planning and scheduling decisions that are made with respect to all activities, operations and organizations involved in the clinical trial process, including the production of the active ingredient (API), new drug, placebo and comparator manufacture, packaging and labeling, distribution to the clinical sites, administration and disposition of unused clinical trials material. Furthermore, the existence of various sources of uncertainty, including processing time, yield rate and the enrollment of volunteers in the trails, makes it hard to meet the needs of the clinical sites. Reasonable amount of materials need to be manufactured and distributed to fully satisfy the enrolled patients, while avoiding oversupply since unused drug products can not be recycled or reshipped to other sites.   

       The decisions in supply chain management can be divided into three hierarchical levels: strategic planning, operational planning and scheduling, and each deal with issues across different timescales. A number of different approaches are being pursued at the strategic level to reduce clinical trial costs, including innovations in trial organization and patient pool selection. In this work we focus attention not on the design of the trials themselves but on more effective centralized operational planning and scheduling for the whole clinical trial supply chain. To accomplish this, this study uses and integrates several key components: a demand simulation, a two-level deterministic planning and scheduling model, and a supply chain discrete event simulation to assess s and improve the robustness of the entire supply chain under different sources of uncertainties.

        The two-level centralized planning and scheduling for the whole supply chain is accomplished using Mixed-Integer-Linear-Programs (MILP) models which are constructed using GAMS and solved using CPLEX. First, a centralized planning model for the whole clinical trial supply chain across a longer time horizon is solved and provides aggregated production and distribution targets for the scheduling level model. The scheduling level model spans a shorter time horizon and deals in greater detail with the scheduling of all the tasks and allocation of resources.. The resulting batch production and associated packaged product distribution plans serve as drivers for the execution of the entire supply chain simulation, which is constructed using discrete event simulation software ExtendSim. This simulation captures all activities, operations and processes involved in the clinical trials. The quality and robustness of the plans are assessed via suitably replicated simulation runs using Monte Carlo sampling of the uncertain parameters. Supply chain performance can be improved by optimization of key global system design parameters as part of an external loop of the Sim-Opt cycle. Results are reported with an industrially motivated case problem, including examination of alternative safety stock levels.