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Application of the Non-dominated Sorting Genetic Algorithm II in Multi-objective Optimization of Orally Disintegrating Tablet Formulation

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Abstract

In the context of increasing application of modelling methods in the field of pharmaceutics, this study aims to reduce the weight of sildenafil orally disintegrating tablets (ODTs) and optimize their formulation through modelling methods. To achieve the goal, the back-propagation neural network (BPNN)–based non-dominated sorting genetic algorithm II (NSGA-II) was introduced to establish the models and to optimize the percentage of magnesium stearate (MgSt), crospovidone (PVPP), and croscarmellose sodium (CCNa) to obtain satisfactory candidate ODTs. Ultimately, the bioequivalence trial was conducted to verify the effectiveness of the formulation. With the support of the neural network, the model showed satisfactory results in the prediction of hardness and disintegration time of ODTs, and the pareto front obtained by the NSGA-II suggested that there was a strong “competition” between disintegration time and hardness. Since disintegration time should be given the priority, the optimal formulation was determined as 1% MgSt, 6% CCNa, and 2.6% PVPP. The bioequivalence trial results indicated a bioequivalence between the test and the reference formulations of sildenafil, and better medication experience for the test formulation. A bioequivalent formulation with better medication experience is successfully prepared using the NSGA-II. It proves that the NSGA-II is applicable to multi-objective optimization of the drug formulation.

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Data Availability

All data generated or analysed during this study are included in this published article and its supplementary information files.

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Acknowledgements

Sincere thanks are given to Zhejiang Heze Pharmaceutical Co., Ltd., for the technical advice on drug production, and Evan Hao from Amazon (Seattle) for the modelling support.

Funding

This work was supported by the Science and Technology Project of Guangdong Province (grant numbers 201802010047) and Biomedical Innovation Institution of Hong Kong & Guangdong Pharmaceutical University.

Author information

Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Advanced Drug Delivery Systems, Guangdong Pharmaceutical University, Guangzhou, 510000, China

    Jiaqi Zhang, Yu Yao, Wei Sun & Huaqing Lin

  2. Hangzhou Yiyuan Pharmaceutical Technology Co., Ltd., Hangzhou, 310000, China

    Liling Tang

  3. Shanghai Branch, Du Pont China Holding Co., Ltd., Shanghai, 200000, China

    Xiaodong Li

Authors
  1. Jiaqi Zhang
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  2. Yu Yao
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  3. Wei Sun
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  4. Liling Tang
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  5. Xiaodong Li
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  6. Huaqing Lin
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Contributions

Jiaqi Zhang: conceptualization, methodology, data curation, writing – original draft, Writing—review and editing.

Yu Yao: investigation, data curation, validation, quality control.

Wei Sun: investigation, data curation, validation.

Liling Tang: clinical project administration

Xiaodong Li: funding acquisition, resources, project administration

Huaqing Lin: funding acquisition, supervision, project administration.

Corresponding author

Correspondence to Huaqing Lin.

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Compliance

The study was conducted in compliance with the Declaration of Helsinki and the International Conference on Harmonization Good Clinical Practice guidelines and was approved by the local ethics committee at Hangzhou Comback Hospital (EC-2021042202). All local regulatory requirements were followed. All subjects provided written informed consent. The study was conducted at the clinical research unit (CRU) in Hangzhou Comback Hospital, Hangzhou, Zhejiang, China.

Conflict of Interest

The authors declare no competing interests.

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Zhang, J., Yao, Y., Sun, W. et al. Application of the Non-dominated Sorting Genetic Algorithm II in Multi-objective Optimization of Orally Disintegrating Tablet Formulation. AAPS PharmSciTech 23, 224 (2022). https://doi.org/10.1208/s12249-022-02379-6

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  • DOI: https://doi.org/10.1208/s12249-022-02379-6

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