Interpretable XGBoost-SHAP Model Predicts Nanoparticles Delivery Efficiency Based on Tumor Genomic Mutations and Nanoparticle Properties

Understanding
the complex interaction between nanoparticles (NPs)
and tumors in vivo and how it dominates the delivery efficiency of
NPs is critical for the translation of nanomedicine. Herein, we proposed
an interpretable XGBoost-SHAP model by integrating the information
on NPs physicochemical properties and tumor genomic profile to predict
the delivery efficiency. The correlation coefficients were 0.66, 0.75,
and 0.54 for the prediction of maximum delivery efficiency, delivery
efficiency at 24 and 168 h postinjection for test sets. The analysis
of the feature importance revealed that the tumor genomic mutations
and their interaction with NPs properties played important roles in
the delivery of NPs. The biological pathways of the NP-delivery-related
genes were further explored through gene ontology enrichment analysis.
Our work provides a pipeline to predict and explain the delivery efficiency
of NPs to heterogeneous tumors and highlights the power of simultaneously
using omics data and interpretable machine learning algorithms for
discovering interactions between NPs and individual tumors, which
is important for the development of personalized precision nanomedicine.