Modeling and closed‐loop control of particle size and initial burst of PLGA biodegradable nanoparticles for targeted drug delivery

Abstract

An in-house computer code based on artificial intelligence has been developed and applied in modeling and closed-loop optimization of release behavior of Poly(lactic-co-glycolic acid) (PLGA) biodegradable particles. A series of micro-and nanoparticles were prepared via water-in-oil-in-water double emulsion to be loaded with albumin-fluorescein isothiocyanate conjugate as a typical drug. The interrelationship between input variables (molecular weight of polymer and stabilizer, polymer concentration, and sonication rate) and outputs (PLGA particle size and percentage of initial burst) was uncovered with the aid of artificial neural network modeling. The regression analysis confirmed acceptable correlation coefficients for the aforementioned responses, where the PLGA molecular weight played the most important role among the studied variables. Input variables needed to minimize PLGA size and PLGA initial burst were then obtained via multiobjective optimization performed by a genetic algorithm. PLGA nanoparticles were checked for particle size and particle size distribution using scanning electron micrographs.

Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine

“…Other computational methods considered the optimal shape and size for nanoparticle binding and drug release . For example, an ANN was used for predicting the size and initial burst rates of poly(lactic‐ co ‐glycolic acid) (PLGA) nanoparticles . The ANN received an input of the PLGA molecular weight and concentration, in addition to the poly(vinyl alcohol) molecular weight and the sonication rate which were used in the preparation process, and returned the predicted nanoparticle size and initial burst rate.…”

Intelligent Monte Carlo: A New Paradigm for Inverse Polymerization Engineering

“…As mentioned earlier, artificial intelligence techniques are very versatile and effective stochastic modeling and optimization tools currently employed successfully in many scientific fields . Among different computationally intelligent techniques, artificial neural networks (ANNs, biologically inspired modeling tools) and genetic algorithms (GAs, evolutionary optimization methods) have gained much attention in recent years . IMC simulation is the application of artificial intelligence within the KMC framework in order to map the desired chain microstructures to input reaction recipe/conditions.…”

Heuristic Search Strategy for Transforming Microstructural Patterns to Optimal Copolymerization Recipes

“…Principally, in solving a multiobjective problem, difficulties may arise from the execution of the searching and decision‐making phases of optimization. This requires development of sophisticated computer codes and hybridization of computational algorithms in order to find the best optimal conditions and subsequently warrant multivariable optimization of two or more targets …”

“…Among different genetic algorithms, NSGA‐II is a unique multiobjective version of the family established primarily based on the domination concept. Undoubtedly, it can be considered as one of the most applied optimization techniques in different fields of science and technology …”

“…Among all “computationally intelligent” optimization methods and also classical deterministic and stochastic optimization techniques, the Non‐dominated Sorting Genetic Algorithm (NSGA‐II) is one of the most powerful methods to handle various multiobjective problems concurrently. Although NSGA‐II has been successfully applied to manage multiobjective optimization problems in different fields of study, it has never been employed in the manipulation of copolymer microstructure …”