Are similar nanoforms comparable in biodistribution? Data-driven approaches for optimizing nanomedicine design

Rational design of tumor-targeted nanomedicines requires systematic understanding of how nanoform properties govern tissue-level biodistribution, yet quantitative frameworks linking physicochemical and study design factors to delivery efficiency remain elusive. In this study, we integrate univariate screening, multivariate permutational analysis, and unsupervised multivariate statistical analysis to dissect biodistribution determinants across six tissues (tumor, liver, spleen, lung, kidney, heart), measured as percentage of injected dose (%ID), in a curated dataset of 200 nanoforms including organic, inorganic, and hybrid materials tested in tumor-bearing mice. Our findings demonstrate that cancer type, core material, hydrodynamic diameter, zeta potential, and dose are primary drivers of tissue-level distribution patterns. Endpoint correlations reveal tight liver-spleen coupling (ρ = 0.76) but minimal tumor correlation with off-target organs (ρ < 0.40), indicating tumor delivery is governed by mechanisms partially distinct from systemic clearance. Strikingly, tumor-efficient nanoforms clustered within a narrow candidate design region: intermediate size (100-440 nm), moderate dose (1-50 mg/kg), and near-neutral to weakly negative surface charge (-15 to +10 mV), with higher tumor %ID (up to 9%) and relatively low-to-moderate liver/spleen uptake. These ranges should be interpreted as approximate regions of interest derived from retrospective data rather than fixed design boundaries, and they require prospective validation before being used as prescriptive rules. Overall, this quantitative design-biodistribution landscape supports hypothesis-driven nanoform grouping for regulatory frameworks, and identifies underexplored design regions for future clinical translation, providing a preliminary and explicitly hypothesis-generating basis for further study.