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Application of Bayesian Optimization for Pharmaceutical Product Development

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Abstract

Purpose

Bayesian optimization has been studied in many fields as a technique for global optimization of black-box functions. We applied these techniques for optimizing the formulation and manufacturing methods of pharmaceutical products to eliminate unnecessary experiments and accelerate method development tasks.

Method

A simulation dataset was generated by the data augmentation from a design of experiment (DoE) which was executed to optimize the formulation and process parameters of orally disintegrating tablets. We defined a composite score for integrating multiple objective functions, physical properties of tablets, to meet the pharmaceutical criteria simultaneously. Performance measurements were used to compare the influence of the selection of initial training sets, by controlling data size and variation, acquisition functions, and schedules of hyperparameter tuning. Additionally, we investigated performance improvements obtained using Bayesian optimization techniques as opposed to random search strategy.

Results

Bayesian optimization efficiently reduces the number of experiments to obtain the optimal formulation and process parameters from about 25 experiments with DoE to 10 experiments. Repeated hyperparameter tuning during the Bayesian optimization process stabilizes variations in performance among different optimization conditions, thus improving average performance.

Conclusion

We demonstrated the elimination of unnecessary experiments using Bayesian optimization. Simulations of different conditions depicted their dependencies, which will be useful in many real-world applications. Bayesian optimization is expected to reduce the reliance on individual skills and experiences, increasing the efficiency and efficacy of optimization tasks, expediting formulation and manufacturing research in pharmaceutical development.

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Abbreviations

ANN:

Artificial neural network

DoE:

Design of experiment

TS:

Tensile strength

TM replaces TS:

Thompson sampling

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Authors and Affiliations

  1. Pharmaceutical Science and Technologies Functional Unit, Medicine Creation Center, Eisai Co, Ltd., 1 Kawashimatakehaya-machi, Kakamigahara, Gifu, 501-6195, Japan

    Syusuke Sano & Susumu Kimura

  2. Data Science Laboratory, hhc Data Creation Center, Eisai Co, Ltd., 5-1-3 Tokodai, Tsukuba, 300-2635, Japan

    Tadashi Kadowaki

  3. Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5-CB02 Kashiwanoha, Kashiwa, 277-8561, Japan

    Koji Tsuda

  4. Center for Advanced Intelligence Project, RIKEN, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan

    Koji Tsuda

  5. Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki, 305-0047, Japan

    Koji Tsuda

Authors
  1. Syusuke Sano
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  2. Tadashi Kadowaki
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  3. Koji Tsuda
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  4. Susumu Kimura
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Correspondence to Syusuke Sano.

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Sano, S., Kadowaki, T., Tsuda, K. et al. Application of Bayesian Optimization for Pharmaceutical Product Development. J Pharm Innov 15, 333–343 (2020). https://doi.org/10.1007/s12247-019-09382-8

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  • DOI: https://doi.org/10.1007/s12247-019-09382-8

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