(496a) 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.