Title: A new joint process optimisation methodology for pharmaceutical destructive characteristics: a statistical approach

Authors: Nguyen Khoa Viet Truong; Vo Thanh Nha; Chul-Soo Kim; Rod Lester L. Dizon; Sung Hoon Hong; Sangmun Shin

Addresses: Department of Systems Management and Engineering, Inje University Gimhae, KN 621-749, South Korea. ' Department of Systems Management and Engineering, Inje University Gimhae, KN 621-749, South Korea. ' Department of Computer Science, Inje University Gimhae, KN 621-749, South Korea. ' Department of Computer Science, Inje University Gimhae, KN 621-749, South Korea. ' Department of Industrial and Information Systems Engineering, Chonbuk National University, Jeonju, Jeonbuk, 651-756, South Korea. ' Department of Systems Management and Engineering, Dong-A University, Busan, 604-714, South Korea

Abstract: In current pharmaceutical research and development, not many robust design (RD) and tolerance design (TD) approaches have been applied, although many researchers and practitioners have realised the importance of process design concepts. Pharmaceutical characteristics often involve different types of destructive measurements, such as hardness, friability, and disintegration in drug development and manufacturing processes. The primary objective of this paper is to develop an integrated robust-tolerance design methodology for handling destructive quality characteristics on pharmaceutical study. A statistical TD optimisation method, which incorporates consumer and producer risk, is proposed that uses a surrogate variable that is strongly correlated with the destructive quality characteristic. Finally, a pharmaceutical case study is performed for verification purposes. In the case study, a comparison between two RD optimisation models (i.e., dual-response and mean squares error models) is also conducted.

Keywords: process optimisation; robust design; tolerance design; destructive quality characteristics; pharmaceutical industry; pharmaceutical R&D; research and development; drug manufacturing; drug development; process design; statistical optimisation.

DOI: 10.1504/IJEDPO.2012.049295

International Journal of Experimental Design and Process Optimisation, 2012 Vol.3 No.2, pp.159 - 177

Available online: 29 Sep 2012 *

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