As adaptive clinical
trials (ACTs) receive growing attention and exhibit promising performance in
practical trials during last decade, they also present challenges to drug
supply chain management. As indicated by Burnham et al. (2015), the challenges
include the uncertainty of maximum drug supply needed, the shifting of supply
requirement, and rapid availability of new supply at decision points. To
facilitate drug supply decision making and the development of mathematical analysis
tools, we propose two trial supply chain optimization problems that represent
different mindsets in response to trial adaptations. In the first problem, we
treat the impacts of ACTs as exogenous uncertainties and study important
aspects of trial supply, including drug wastage, resupply policy, trial length,
and costs minimization, via a two-stage stochastic program. In the second
problem, we incorporate the adaptation rules of ACTs with supply chain
management and numerically study the impact of joint optimization on the trial
and drug supply planning through a mixed-integer nonlinear program (MINLP). For
solution approaches to the problems, we use progressive hedging algorithm (PHA)
and particle swarm optimization (PSO) respectively, and take advantages of the
problem structures to enhance the solution efficiency. With case studies, we
see that the proposed models capture the features of ACT drug supply and the
mechanisms of trial conduction well. The solutions not only reflect the impact
of trial adaptations but also provide managerial suggestions, e.g. the
prediction of needed production amount, storage capacity at clinical sites, and
resupply schemes. The joint optimization also suggests a new angle and research
extension in the field of ACT design and supply.
Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/9956177 |
Date | 17 October 2019 |
Creators | Wei-An Chen (7474730) |
Source Sets | Purdue University |
Detected Language | English |
Type | Text, Thesis |
Rights | CC BY 4.0 |
Relation | https://figshare.com/articles/DRUG_SUPPLY_CHAIN_OPTIMIZATION_FOR_ADAPTIVE_CLINICAL_TRIALS/9956177 |
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