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A Strategy for the Effective Optimization of Pharmaceutical Formulations Based on Parameter-Optimized Support Vector Machine Model

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Abstract

Engineering pharmaceutical formulations is governed by a number of variables, and the finding of the optimal preparation is intricately linked to the exploration of a multiparametric space through a variety of optimization tasks. As a result, making such optimization activities simpler is a significant undertaking. For the purposes of this study, we suggested a prediction model that was based on least square support vector machine (LSSVM) and whose parameters were optimized using the particle swarm optimization algorithm (PSO-LSSVM model). Other in silico optimization methods were used and compared, including the LSSVM and the back propagation (BP) neural networks algorithm. PSO-LSSVM demonstrated the highest performance on the test dataset, with the lowest mean square error. In addition, two dosage forms, quercetin solid dispersion and apigenin nanoparticles, were selected as model formulations due to the wide range of formulation compositions and manufacturing factors used in their production. Three different models were used to predict the ideal formulations of two different dosage forms, and in real world, the Taguchi orthogonal design arrays were used to optimize the formulations of each dosage form. It is clear that the predicted performance of two formulations using PSO-LSSVM was both consistent with the outcomes of the Taguchi orthogonal planned experiment, demonstrating the model’s good reliability and high usefulness. Together, our PSO-LSSVM prediction model has the potential to accurately predict the best possible formulations, reduce the reliance on experimental effort, accelerate the process of formulation design, and provide a low-cost solution to drug preparation optimization.

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Abbreviations

OFAT:

One-factor-at-a-time method

DoE:

Design of experiment

RSM:

Response surface methodology

LSSVM:

Least square support vector machines

BP:

Back propagation neural networks algorithm

PSO:

Particle swarm optimization algorithm

PSO-LSSVM:

LSSVM optimized by the particle swarm optimization algorithm

ANOVA:

Analysis of variance

PVPK30:

Polyvinylpyrrolidone K30

MSE:

The mean square error

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Acknowledgements

This work was supported, in part, by the National Natural Science Foundation of China (No. 82074286), Natural Science Foundation of Jiangsu Province (Nos. BK20191428, BK20181445), Six Talent Peak Project from Government of Jiangsu Province (No. SWYY-013), and the Scientific Research Foundation of Jiangsu University (No. 12JDG034, 14JDG163).

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  1. Siqi Wang and Jianping Yang contributed equally to this work.

Authors and Affiliations

  1. School of Pharmacy, Jiangsu University, 301 Xuefu Road, Zhenjiang, 212013, Jiangsu Province, China

    Siqi Wang, Hengwei Chen, Kexin Chu, Xuefei Yu, Yaqiong Wei, Haixia Zhang, Mengjie Rui & Chunlai Feng

  2. Changzhou Hospital of Traditional Chinese Medicine, Changzhou, 213003, Jiangsu Province, China

    Jianping Yang

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Contributions

S. W and J. Y contribute to the conception of the work. H. C, K. C, X. Y, Y. W, and H. Z conducted the work. And H. C, and K. C analyzed the data for the work. M. R and C. F approved the version to be published.

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Correspondence to Mengjie Rui or Chunlai Feng.

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Wang, S., Yang, J., Chen, H. et al. A Strategy for the Effective Optimization of Pharmaceutical Formulations Based on Parameter-Optimized Support Vector Machine Model. AAPS PharmSciTech 23, 66 (2022). https://doi.org/10.1208/s12249-022-02210-2

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