Multistep Machine Learning Pipeline For Polymeric Nanoparticle Design.

Area of Science:

• Nanotechnology
• Drug Delivery Systems
• Machine Learning Applications

Background:

• Integrating machine learning (ML) into nanotechnology offers a path for rational design and faster development of drug delivery systems.
• Current studies in this area are limited and present methodological challenges.
• Nanoparticle formulation development often involves extensive experimental work.

Purpose of the Study:

• To present a modular machine learning (ML) pipeline for predictive modeling of nanoparticles.
• To optimize the nanoprecipitation process for drug delivery systems using isoniazid as a model drug.
• To reduce experimental workload and enhance systematic formulation development.

Main Methods:

• A three-step ML pipeline was developed: binary classification for nanoparticle formation, multiclass classification for size ranges, and regression for size refinement.
• Algorithms evaluated included Extreme Gradient Boosting, Random Forest, Artificial Neural Networks (ANN), Generalized Linear Models, and Naive Bayes.
• Iterative experimental rounds with model retraining and virtual formulation simulation guided optimization.

Main Results:

• Artificial Neural Networks (ANN) demonstrated superior performance, achieving R² > 0.9 in classification and regression tasks.
• The ML pipeline accurately predicted nanoparticle size within a broad range (75-768 nm) with low error (<40 nm).
• Validation confirmed the model's reliability and generalization capacity for new formulations.

Conclusions:

• The proposed ML pipeline enables data-driven decision-making in nanopharmaceutical research.
• This approach supports systematic formulation development aligned with Quality-by-Design principles.
• The scalable framework can significantly accelerate the development of advanced drug delivery systems.