Descriptor-Guided Selection of Extracellular Vesicle Loading Strategies for Small-Molecule Drug Delivery: A...

Area of Science:

• Nanotechnology
• Drug Delivery Systems
• Biomaterials Science

Background:

• Extracellular vesicles (EVs) are promising nanocarriers for drug delivery.
• Current methods for loading small-molecule drugs into EVs rely heavily on empirical screening.
• Lack of a mechanistically grounded framework leads to inefficient, resource-intensive experimentation.

Purpose of the Study:

• To develop a descriptor-informed framework for rational prioritization of EV loading methods.
• To reduce the reliance on empirical screening in early-stage EV formulation development.
• To enable efficient selection of optimal loading strategies for small-molecule cargos.

Main Methods:

• Assembled a dataset of 21 compounds with experimentally determined loading efficiencies across five EV loading methods.
• Calculated seven physicochemical descriptors for each compound.
• Trained Elastic Net regression models for each loading strategy, focusing on decision-level ranking.

Main Results:

• Continuous regression performance was modest (LOOCV R² = 0.06-0.41).
• Decision-level accuracy for identifying the optimal loading method was high (75-80.5%).
• Mechanical disruption methods showed greater predictive stability; applicability domain was confirmed.

Conclusions:

• Established a mechanistically interpretable, descriptor-based decision-support framework for EV loading.
• The framework reliably prioritizes loading strategies, reducing experimental burden in EV formulation.
• Provides a quantitative basis for rational EV-based drug delivery design.