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Quality by design approach with multivariate analysis and artificial neural network models to understand and control excipient variability

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Abstract

Purpose

Although understanding the variability in the physicochemical properties of an excipient in a drug formulation is becoming an important aspect of the quality by design approach, few studies have reported the effect of excipient variability on design space. This study aimed to understand how the variability of excipient physicochemical properties caused by changes in manufacturer and grade influence the tablet quality.

Methods

In the quality by design approach, the formulation of the immediate-release tablet was optimized with a D-optimal mixture design. Subsequently, polyvinylpyrrolidone of different grades and manufacturers, which is used as a binder, was used to confirm the variability within the design space. The main cause of variability was identified by multivariate analysis, and a predictive model using an artificial neural network was developed to predict the dissolution profile.

Results

The design space greatly shifted the grade changes, mainly because of variabilities in the K values and particle size distribution that are strongly correlated with critical quality attributes. The predictive model accurately predicted the dissolution profile with low absolute and relative errors.

Conclusion

These findings highlight the importance of understanding and controlling excipient variability in the quality by design approach.

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Funding

This work was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education [Grant Number 2020R1I1A307373311].

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Authors and Affiliations

  1. Department of Pharmaceutical Engineering, Inje University, Gimhae-si, 50834, Republic of Korea

    Ji Yeon Kim & Du Hyung Choi

  2. Department of Pharmaceutical Engineering, Inje University, Gimhae-si, 50819, Korea

    Du Hyung Choi

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  1. Ji Yeon Kim
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Correspondence to Du Hyung Choi.

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Kim, J.Y., Choi, D.H. Quality by design approach with multivariate analysis and artificial neural network models to understand and control excipient variability. J. Pharm. Investig. 53, 389–406 (2023). https://doi.org/10.1007/s40005-022-00608-5

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