Artificial intelligence approaches for predicting the stability and encapsulation efficiency of lipid-based nanocarriers

Abstract

Lipid-based nanocarriers (LBNs), including liposomes, solid lipid nanoparticles (SLNs), nanostructured
lipid carriers (NLCs), and lipid nanoparticles (LNPs), represent a cornerstone in nanomedicine for
targeted drug delivery. However, traditional Design of Experiments (DoE) struggles with the complexity
of multi-component lipid formulations, hindering optimization of encapsulation efficiency (EE) and
stability key metrics governed by thermodynamic drug-lipid interactions, critical material attributes
(CMAs) like lipid ratios and surfactants, and critical process parameters (CPPs) such as manufacturing
techniques (e.g., microfluidics vs. thin-film hydration). This review explores artificial intelligence (AI)
as a paradigm shift toward data-driven rational design. Covering the AI workflow from data curation
(literature mining, high-throughput screening) and feature engineering (SMILES, molecular
fingerprints) to model deployment we evaluate algorithms including Multiple Linear Regression (MLR),
Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Networks (ANNs), and deep
learning for predicting EE (via drug-lipid compatibility, partition coefficients) and stability (particle size,
zeta potential, shelf-life). Case studies demonstrate AI's success in forecasting high EE for challenging
drugs (hydrophilic molecules, biologics) and stability under storage stresses. Advanced strategies like
generative models (GANs, VAEs), evolutionary algorithms (GA, PSO), and active learning reduce wet-
lab trials. Challenges persist: data scarcity, black-box interpretability, generalizability, and in vitro-to-in
vivo translation. Future trends include physics-informed neural networks (PINNs), federated learning,
and self-driving labs for autonomous optimization, bridging computational sciences with pharmaceutical
nanotechnology.