Pharmaceutical clinical trial supply chain management

New drug development follows an extended sequence of steps (discovery, animal trials, FDA application (IND), product and process development, three phases of clinical trials, FDA filing and approval and launch) as a result of which it takes many years and considerable expense (upwards of $1 Billion) to bring a new drug to market. The clinical trials constitute a critically important and very expensive part of the development process as it involves producing, distributing and administering the candidate therapy to an increasing number of patients located in different geographic zones. A number of different approaches are being pursued to reduce clinical trial costs, including innovations in trial organization and patient pool selection. This thesis focuses attention not on the design of trials but on more effective management of the clinical trial supply chain. The key challenges is to fully satisfy the stochastic demands of clinical sites, while minimizing oversupplies and leftovers since unused materials cannot be recycled or reshipped to other sites at the end of clinical trials. Moreover, most supply chain management research deals with uncertainties in the planning models by using expected values of uncertainties to generate period plans to buffer the effects of uncertainties. However, the horizon of a clinical trial supply chain is only 1-2 years, at which point the trial is terminated. In traditional commercial supply chains the horizon can extend to 10 years or more and there is no explicit point at which the supply chain is terminated and residual material in inventory was discarded Therefore, the strategy used to buffer uncertainties in commercial supply chains become ineffective as expected values cannot be effectively used as targets. In this thesis, a simulation-optimization approach combined with risk pooling strategy, keeping a centralized inventory at a distribution center instead of with several retailers, is presented for clinical trial supply chain management to increase its operation efficiency and reduce the cost. The entire approach includes four modules; namely, demand forecasting which mimics the stochastic patient arrival process, a decentralized or integrated planning and optimization which formulates and solves a Mixed-Integer-Linear-Programs (MILP) based planning and scheduling problem, a discrete event simulation model which simulates the entire clinical trial supply chain, and an outer loop search process to optimize the pooled safety stock levels. The operational plans developed via the MILP planning model serve as driver for the execution of the discrete event simulation of the supply chain, and the sum of deviations from target CSL is reduced by optimizing the pooled safety stock levels and re-executing the Simulation-Optimization cycle. Case studies are reported and compared to demonstrate the utility of the proposed approach and the combined risk pooling strategy