رویکرد بهینه سازی شبیه سازی برای مدیریت زنجیره تامین کارآزمایی بالینی با استفاه از سناریوی پیش بینی تقاضا
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|9799||2012||15 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Computers & Chemical Engineering, , Volume 40, 11 May 2012, Pages 82-96
In the pharmaceutical industry, the development activities that are required to bring a new drug to market involve considerable expense (upwards of $1 Billion) and can take in excess of 10 years. Clinical trials constitute a critically important and very expensive part of this development process as the associated supply chain encompasses producing, distributing and administering the candidate therapy to volunteer patients located in different geographic regions. A number of different approaches are being pursued to reduce clinical trial costs, including innovations in trial organization and patient pool selection. In this work, we focus our attention on improved management of the clinical supply chain. A simulation-optimization approach is presented, including patient demand simulation and demand scenario forecast, mathematical programming based planning, and discrete event simulation of the entire supply chain. Three case studies with different demand types are reported and compared to demonstrate the utility of the proposed approach.
New drug development follows an extended sequence of steps, including discovery, animal trials, FDA application, product and process development, three phases of clinical trials, FDA filing and approval, and launch. As a result it takes many years and considerable expense (upwards of $1 Billion) to bring a new drug to market. The discovery phase aside, clinical trials constitute a very expensive part of the development process. Normally, clinical trials with different test objectives (e.g. safety, efficacy, side effects) are conducted at the same time to expedite the new drug development process, thus further complicating clinical trial supply chain management. It has become more and more critical to have an optimized clinical trials supply chain management process to accelerate the new drug development process and reduce the total cost for a pharmaceutical company. A substantial amount of work has been reported in the process industry supply chain management and optimization area, but only a limited literature has addressed the issues faced in the pharmaceutical industry. Shah (2004) provided an overview of the state of pharmaceutical industry related research and analyzed the key issues and strategies for pharmaceutical supply chain optimization. From Shah's review paper, there are research activities in discovery pipeline and development management, process development and plant design, as well as production planning and scheduling, but few references can be found in the clinical supply chain management area. There does exist literature that addresses issues such as the design of clinical trials, patient pool selection, and the drug delivery process. For example, Monkhouse, Carney, and Clark (2006) discussed the design and development of clinical trials in some detail and provided the basic knowledge necessary to conduct pharmaceutical clinical trials. Byron, 2002a and Byron, 2002b introduced the interactive voice response (IVR) systems, which are commonly used to manage clinical trial drug delivery process. Dowlman et al. (2004) proposed the use of a simulation model of an IVR-managed supply system to evaluate the outcomes and select the best strategy among a pre-defined supply strategy pool including a variety of supply strategies and scenarios. However, the management of an entire clinical trial supply chain and approaches to reducing its operational cost have not been addressed in the above references and has limited attention to date. Abdelkafi, Beck, David, Druck, and Horoho (2009)’s work is closely related with our work. In that work an approach to selecting the best supply plan was reported which attempted to balance the costs and the risk of short supply, and the Bayesian principle was utilized to reevaluate supply strategies over time. However, Abdelkafi's work only focuses on the drug supply part without considering the manufacturing part, which is also important in a clinical trial supply chain management problem. Traditionally, the pharmaceutical industry uses batch processes to manufacture pharmaceutical products both at the pilot and the commercial scale. Since these batch facilities are usually shared across various products, especially for the quantities needed for clinical trials, it is necessary to decide the order, amount and timing of the products to be produced on these shared resources. These decisions can have a large economic impact on the company at the clinical trials stage, because missing the delivery of trial dosage to patients can significantly delay completion of the trial and hence delay the time to market which in turn can mean significant loss of revenue. Therefore, the key technical challenges in managing a clinical trial supply chain are to meet the needs of the clinical sites, so that patients are fully supplied once they are enrolled while minimizing total operating cost. Deterministic mixed integer linear programs (MILP) optimization methods have been proposed to solve resource constrained project planning and scheduling problems (see Floudas & Lin, 2004, which presented a comprehensive review of these approaches). Most of the work reported is confined to a deterministic context; however, for a clinical trial supply chain, not only patient enrollment is highly variable, but uncertainties also arise in manufacturing and shipment lead times, in process failures and in production yields. Jain and Grossman (1999) addressed the problem of a single project with no resource constraints and task success uncertainty. Honkomp (1998) dealt with the problem of both project selection and project scheduling with task success uncertainty. Most of these and related references take into account stochastic elements by incorporating expected value of uncertainties into planning models to generate period plans that buffer the effect of uncertainties. However, the horizon of a clinical trial materials supply chain (∼1–2 years), is significantly shorter than that of a commercial supply chain, (∼10+ years). Therefore, the strategies utilized to buffer the uncertainties in commercial supply chains become ineffective as expected values cannot be effectively used as targets. Moreover, a typical clinical trial is terminated after 1–2 years and thus the drugs leftover at the end of clinical trial must be treated as wastage since unused materials cannot be reused and must be disposed. By contrast in traditional supply chain planning horizon lasts for more than 10 years and the leftover inventory at the end of the horizon are not considered to be a significant cost. Instead the focus is on average inventory cost. In the pharmaceutical industry, the cost of unused clinical trial dosage can be quite large and thus there is significant benefit in reducing this cost through more efficient supply chain planning while also balancing this against the costs of failure to supply the dosage needs of clinical trial patients at the times and amounts required by the trial protocol. Moreover, modern pharmaceutical companies are apt to develop complex clinical trial supply chains in order to efficiently meet the needs of clinical sites and expedite the execution of the clinical trials. It is likely that several clinical trials targeting various objectives will be conducted in parallel. The need for approaches to obtain the optimal solution to such a complex, stochastic optimization problem has motivated the development of dynamic programming and simulation optimization strategies. Blau, Pekny, Varma, and Bunch (2004) presented a simulation model to maximize the expected economic returns in the process of selecting new products in the pharmaceutical industry. Subramanian, Pekny, and Reklaitis (2001) propose a computational architecture called “Sim-Opt”, which combines mathematical programming and discrete event system simulation to assess the uncertainty and control the risk present in the new product development pipeline problem. Jung, Blau, Pekny, Reklaitis, and Eversdyk (2004) reported a simulation optimization approach to solve a generalized supply chain problem under demand uncertainty. These reports demonstrate that simulation based optimization methods can be an efficient and effective alternative to solving large stochastic decision problems. In this work, we present an integrated simulation-optimization computational framework, which includes the stochastic demand forecasting part to generate different demand scenarios, a mathematical programming based planning model which can accommodate different demand scenarios to optimize the production and distribution cost, and a discrete event simulation of the entire supply chain to capture uncertainties.
نتیجه گیری انگلیسی
The pharmaceutical clinical trial supply chain management problem discussed in this paper, is characterized by a sequence of planning and scheduling decisions that are made in order to better synchronize all activities, operations and organizations involved in the clinical trial process. A simulation-optimization computational framework, which combines stochastic demand forecasting, decentralized planning model capable of pooling demand profiles with different probabilities of realization and discrete event simulation, has been proposed. The quality and robustness of the plans generated by the planning model are assessed by replicated simulation runs, which were executed in reasonable computational times by importing production and distribution plans generated from the planning models. This approach was demonstrated via three case studies with different demand variations: low, middle and high, for a worldwide clinical trial supply chain management problem. As shown for this case, it was possible to achieve a reasonably high service level (higher than 90%) using the proposed approach. In general, there is a tradeoff between supply chain costs and CSL which is defined by a Pareto optimal frontier. Given demand uncertainties, that frontier is stochastic rather than deterministic in nature. The simulation based framework presented in this work provides a very flexibly tool for developing this stochastic frontier and thus offers an effective tool for risk management. Secondly, this computational framework can be used to track patient timelines during the clinical trial, and thus offers the potential for some interesting tactical studies. For instance the simulation allows recording of the drug packages previously administered to a patient. If a patient arrives at the clinical site but the required dosage package is not available, then that the patient is being dropped from the clinical trial and thus effectively the value of the previously administered dosage is likewise lost. Such occurrence not only represents a reduction in CSL, but also allows quantification of the cost of undersupply in terms of ineffectively used drug packages. Moreover, while not demonstrated in the case study reported here, the proposed integrated framework also provides considerable flexibility in addressing interesting design problems, such as the selection of clinical sites based on historical performance in patient enrollment and completion and development incentives to improve patient retention to the end of the trial.