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<p>Study explored the synergistic anti-tumor effects of a combination of compounds from Traditional Chinese Medicine, including rosmarinic acid (RA), chlorogenic acid (CA), and scoparone (SCO), in the formulation of multivesicular liposomes (MVLs). Optimization of formulations and process parameters was essential to achieve effective liposomal encapsulation and optimal release profiles for these three compounds with diverse properties. Traditional trial-and-error approaches are inefficient for the optimization of complex multi-compound MVLs. We developed a new formulation optimization model, which could address this issue by predicting the optimal multi-compound MVLs formulation. Our machine learning model integrated support vector machine regression (SVR) algorithm and cuckoo search (CS) algorithm, resulting in three CS-SVR models to predict single-compound MVLs. The CS algorithm, with various weighting rules, was then applied to search the best formulation parameters across three CS-SVR models and to maximize the encapsulation efficiency for all three compounds. The multi-compound MLVs were subsequently prepared under the predicted conditions, achieving an optimized particle size of 15.12 µm, with encapsulation efficiencies of 82.93 ± 2.43% for CA, 82.22 ± 1.25% for RA, and 95.60 ± 0.18% for SCO. The predicted optimal multi-compound MVLs were further validated through <i>in vitro</i> characterization and <i>in vivo</i> anti-tumor experiments, showing a promising synergistic anti-tumor effect consistent with <i>in vitro</i> results. This model accurately predicted optimal encapsulation conditions, which were validated experimentally, demonstrating improved encapsulation efficiencies and reduced trial-and-error iterations. Collectively, our model provides a predictive pathway for multi-compound MVLs formulation, indicating the ability of this model to significantly reduce experimental burden and accelerate formulation development.</p> Graphical Abstract <p></p>

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Computational Design and Optimization of Multi-Compound Multivesicular Liposomes for Co-Delivery of Traditional Chinese Medicine Compounds

  • 期刊论文

  • 发表日期：

    2025年2月11日

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  • Mengjie Rui,
  • Yali Su,
  • Haidan Tang,
  • Yinfeng Li,
  • Naying Fang,
  • Yingying Ge,
  • Qiuqi Feng,
  • Chunlai Feng

摘要

Study explored the synergistic anti-tumor effects of a combination of compounds from Traditional Chinese Medicine, including rosmarinic acid (RA), chlorogenic acid (CA), and scoparone (SCO), in the formulation of multivesicular liposomes (MVLs). Optimization of formulations and process parameters was essential to achieve effective liposomal encapsulation and optimal release profiles for these three compounds with diverse properties. Traditional trial-and-error approaches are inefficient for the optimization of complex multi-compound MVLs. We developed a new formulation optimization model, which could address this issue by predicting the optimal multi-compound MVLs formulation. Our machine learning model integrated support vector machine regression (SVR) algorithm and cuckoo search (CS) algorithm, resulting in three CS-SVR models to predict single-compound MVLs. The CS algorithm, with various weighting rules, was then applied to search the best formulation parameters across three CS-SVR models and to maximize the encapsulation efficiency for all three compounds. The multi-compound MLVs were subsequently prepared under the predicted conditions, achieving an optimized particle size of 15.12 µm, with encapsulation efficiencies of 82.93 ± 2.43% for CA, 82.22 ± 1.25% for RA, and 95.60 ± 0.18% for SCO. The predicted optimal multi-compound MVLs were further validated through in vitro characterization and in vivo anti-tumor experiments, showing a promising synergistic anti-tumor effect consistent with in vitro results. This model accurately predicted optimal encapsulation conditions, which were validated experimentally, demonstrating improved encapsulation efficiencies and reduced trial-and-error iterations. Collectively, our model provides a predictive pathway for multi-compound MVLs formulation, indicating the ability of this model to significantly reduce experimental burden and accelerate formulation development.

Graphical Abstract