Application of multi-objective optimization algorithm to the preparation of polycaprolactone microsphere formulations

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Background Polycaprolactone microspheres (PCL-MS) are widely used in tissue filling, where particle size and uniformity are crucial for improving filling and therapeutic effects. Efficient optimization methods are essential for accelerating their development. Methods Box-Behnken design was applied to investigate three factors for PCL-MS: PCL concentration ( ), polyvinyl alcohol concentration ( ) and water-oil ratio (WOR, ). Mathematical models were developed to predict particle size ( ) and particle size distribution width ( ). Multi-objective optimization of and was performed using the Nondominated Sorting Genetic Algorithm-II (NSGA-II) and the Multi-Objective Artificial Hummingbird Algorithm (MOAHA) to determine the optimal preparation schemes. Experimental validation was conducted to confirm the validity and reliability of the optimization schemes. Results After multi-objective optimization, two ideal preparation schemes were selected from the Pareto solution sets obtained by NSGA-II and MOAHA, respectively. Experimental validation showed no significant statistical difference ( P >0.05) between the measured and predicted values of and for scheme 12 and 21 of NSGA-II and scheme 3 of MOAHA, with deviations under 5%. All protocols met target requirements, indicating their suitability for PCL-MS preparation. Conclusion This study utilized Box-Behnken design and intelligent optimization to develop three alternative PCL-MS formulations, facilitating the production of microspheres with smaller particle sizes and narrower distributions, thereby advancing formulation development.

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