Data-Efficient Design of Experiments for Lipid Nanoparticle Development through Prior-Informed Machine Learning

Experimental design for nanomedicine product de-
velopment is often limited by high experimental costs and
small datasets. Traditional design of experiments (DOE) methods
are typically applied to a single development programme with
a fixed set of parameters, leading to redundant testing and
inefficient exploration of the design space. This work presents a
machine learning (ML) enabled DOE framework that leverages
prior experimental knowledge to guide efficient sampling across
conditions. The framework integrates ML models with adaptive
point selection to identify informative regions of the design space
while minimizing experimental runs. We propose two strategies
for transferring knowledge between conditions: prior-informed
learning, and residual learning, which we compare with the
baseline approach of direct learning, demonstrating that prior-
informed and residual approaches achieve comparable surface
reconstruction accuracy with up to 3 times fewer samples than
direct learning.