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Managing process uncertainties for i...

Dey, Sujoy.

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Managing process uncertainties for increased productivity in pharmaceutical drug development using modeling and simulation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Managing process uncertainties for increased productivity in pharmaceutical drug development using modeling and simulation./
作者:	Dey, Sujoy.
面頁冊數:	347 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-05, Section: B, page: 2761.
Contained By:	Dissertation Abstracts International66-05B.
標題:	Engineering, Industrial. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3175592
ISBN:	0542138476

Managing process uncertainties for increased productivity in pharmaceutical drug development using modeling and simulation.
Dey, Sujoy.

Managing process uncertainties for increased productivity in pharmaceutical drug development using modeling and simulation. - 347 p.

Source: Dissertation Abstracts International, Volume: 66-05, Section: B, page: 2761.

Thesis (Ph.D.)--Stevens Institute of Technology, 2005.

This dissertation demonstrates how uncertainties introduced during the process of drug development can be managed using quantified discrete event simulation. It also shows how the simulation model can be used to impact research and development productivity through capacity analysis. The four key input variables modeled are process time for preclinical and clinical trials, attrition rate as the drug attempts to pass scientific and medical hurdles, human resources, and the direct cost for each phase of development. Cumulative and dependent variability are not easily captured in traditional spreadsheet models, a current standard throughout the pharmaceutical industry, especially when modeling processes whose states change at random points in time. Thus, we use discrete event simulation to handle random times, resources, and dependencies of both. This research increases the visibility of the complexity and variability of the ethical drug development process, and the model enhances long-term system control, productivity, stability, sustainability, and decision support.

ISBN: 0542138476Subjects--Topical Terms:

626639
Engineering, Industrial.

Managing process uncertainties for increased productivity in pharmaceutical drug development using modeling and simulation.
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500 $a Source: Dissertation Abstracts International, Volume: 66-05, Section: B, page: 2761.
500 $a Chairs: Jack M. Kloeber; John V. Farr.
502 $a Thesis (Ph.D.)--Stevens Institute of Technology, 2005.
520 $a This dissertation demonstrates how uncertainties introduced during the process of drug development can be managed using quantified discrete event simulation. It also shows how the simulation model can be used to impact research and development productivity through capacity analysis. The four key input variables modeled are process time for preclinical and clinical trials, attrition rate as the drug attempts to pass scientific and medical hurdles, human resources, and the direct cost for each phase of development. Cumulative and dependent variability are not easily captured in traditional spreadsheet models, a current standard throughout the pharmaceutical industry, especially when modeling processes whose states change at random points in time. Thus, we use discrete event simulation to handle random times, resources, and dependencies of both. This research increases the visibility of the complexity and variability of the ethical drug development process, and the model enhances long-term system control, productivity, stability, sustainability, and decision support.
520 $a The product of this effort is an analytical tool for predicting the effect of changes to existing systems and a design tool to predict the performance of new systems under varying sets of circumstances. This dissertation explores important features of the drug development process in an attempt to identify how and to what extent they affect the pipeline performance. The results show that this dissertation increases the understanding of the impact of selected key variables on productivity and resource capacity of a very complex process of drug development.
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