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Application of model‐based design of experiments for process development of solid oral dosage forms.

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Title: Application of model‐based design of experiments for process development of solid oral dosage forms.
Authors: Ghosh, Kanishka1 (AUTHOR) kanishka.ghosh@lilly.com, García Muñoz, Salvador1 (AUTHOR)
Source: AIChE Journal. Apr2026, Vol. 72 Issue 4, p1-13. 13p.
Subjects: Experimental design, Solid dosage forms, Pharmaceutical industry, Process optimization
Abstract: Design of experiments (DoE) has been used extensively for strategic experimentation and process development in the pharmaceutical industry. Conventional DoE approaches, while foundational, often require extensive resources and do not fully utilize existing system knowledge. In this work, we demonstrate the use of a continuous effort‐driven, discrete model‐based DoE approach that calculates multiple locally optimal experiments from discretized control variable ranges by leveraging existing process knowledge. This workflow enables efficient experimental space exploration and parallel experimentation, reducing development times and material costs significantly and elucidating input–output correlations that may not be obvious without prior knowledge of the process model. Our work establishes that traditional statistical DoE constructs are neither superior nor necessary in advancing process development when an initial process model (prior knowledge) is available. The regulatory expectation that a DoE must resemble a fractional factorial is misled and only driven by legacy practices of empirical process development approaches. [ABSTRACT FROM AUTHOR]
Copyright of AIChE Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 192223910
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Application of model‐based design of experiments for process development of solid oral dosage forms.
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  Data: <searchLink fieldCode="AR" term="%22Ghosh%2C+Kanishka%22">Ghosh, Kanishka</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> kanishka.ghosh@lilly.com</i><br /><searchLink fieldCode="AR" term="%22García+Muñoz%2C+Salvador%22">García Muñoz, Salvador</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22AIChE+Journal%22">AIChE Journal</searchLink>. Apr2026, Vol. 72 Issue 4, p1-13. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Experimental+design%22">Experimental design</searchLink><br /><searchLink fieldCode="DE" term="%22Solid+dosage+forms%22">Solid dosage forms</searchLink><br /><searchLink fieldCode="DE" term="%22Pharmaceutical+industry%22">Pharmaceutical industry</searchLink><br /><searchLink fieldCode="DE" term="%22Process+optimization%22">Process optimization</searchLink>
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  Data: Design of experiments (DoE) has been used extensively for strategic experimentation and process development in the pharmaceutical industry. Conventional DoE approaches, while foundational, often require extensive resources and do not fully utilize existing system knowledge. In this work, we demonstrate the use of a continuous effort‐driven, discrete model‐based DoE approach that calculates multiple locally optimal experiments from discretized control variable ranges by leveraging existing process knowledge. This workflow enables efficient experimental space exploration and parallel experimentation, reducing development times and material costs significantly and elucidating input–output correlations that may not be obvious without prior knowledge of the process model. Our work establishes that traditional statistical DoE constructs are neither superior nor necessary in advancing process development when an initial process model (prior knowledge) is available. The regulatory expectation that a DoE must resemble a fractional factorial is misled and only driven by legacy practices of empirical process development approaches. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of AIChE Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/aic.70204
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      – SubjectFull: Solid dosage forms
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      – SubjectFull: Pharmaceutical industry
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      – SubjectFull: Process optimization
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              Text: Apr2026
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