Analysis of Pharmacokinetic–Pharmacodynamic Relationships of Nanoparticles against Tumors

Abstract

Nanoparticle (NP)-based drug delivery systems hold great promise for cancer treatment. However, designing efficient NP formulations for clinical usage remains a challenge. This study created a “Nano-PKPD Database” by curating pharmacokinetic (PK) data on NP tumor delivery and tissue biodistribution, as well as pharmacodynamic (PD) data on tumor volume changes in tumor-bearing mice. Various machine learning (ML) models were developed to explore the PK–PD relationship and predict antitumor efficacy based on NP physicochemical properties, experimental strategies, and PK metrics. The current database contains 611 data sets from 345 papers on NP time-dependent concentrations in tumors and major organs. The median delivery efficiency was 0.70 percentage of injected dose (%ID) in tumors, 0.17%ID (heart), 10.72%ID (liver), 0.59%ID (spleen), 0.33%ID (lung), and 0.96%ID (kidney). In addition, 833 data sets from 340 papers on time-dependent tumor volume changes were collected, where the median tumor growth inhibition was 63.12%. A total of 18 ML models were developed, where tree-based models achieved the best discriminative performance. The use of assistive technology, zeta potential, and targeting strategy were the top 3 features related to antitumor efficacy. This study reports an open-access database and multiple ML models for PK–PD investigation, facilitating nanomedicine design and accelerating clinical translation.