ArticleACS nano2025
Data-Driven Prediction of Nanoparticle Biodistribution from Physicochemical Descriptors.
Article in ACS nano, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Supramolecular Degraders: An Emerging Paradigm in Targeted Protein Degradation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Cancer Vaccine Development: Toward Artificial Intelligence-Assisted Personalized Cell Membrane Nanovaccine.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Advances in Machine Learning-Enhanced PBPK Models for Brain-Targeted Drug Delivery via Nanocarriers: A Comprehensive Review.Journal of functional biomaterials · 2026Review
- Are similar nanoforms comparable in biodistribution? Data-driven approaches for optimizing nanomedicine design.Materials today. Nano · 2026Article
- Nanomaterial toxicity and risk assessment integrating functionalization strategies advanced in vitro models and regulatory perspectives.Discover nano · 2026Review
- Biogenic Metal Nanoparticles from Indian Flora as Programmable Bio-Interfaces: From Phytochemical Coronas to Precision Nanomedicine.International journal of molecular sciences · 2026Review
- Emerging Technologies and Integrated Interdisciplinary Strategies for Mitigating Protein Aggregation in Therapeutic Formulations.Pharmaceutical research · 2026Review
- Harnessing nanoparticles for phytochemical delivery: a comprehensive review of safety and therapeutic potential.Therapeutic delivery · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nanoparticles have gained significant attention in biomedicine, electronics, and environmental science due to their unique physicochemical properties, which critically influence their absorption, distribution, metabolism, and excretion behavior in biological systems. However, predicting nanoparticle biodistribution and pharmacokinetics remains challenging due to the complexity of biological systems and the reliance on animal-derived data for physiologically based pharmacokinetic (PBPK) modeling. To address these limitations, this study integrates PBPK modeling with quantitative structure-activity (QSAR) relationship principles and multivariate linear regression (MLR) to develop a predictive framework for nanoparticle biodistribution based solely on physicochemical properties, using biodistribution data from healthy mice. Focusing exclusively on nondissolvable nanoparticles, we employed Bayesian analysis with Markov chain Monte Carlo simulations to fit PBPK models and generate kinetic parameters. The MLR-PBPK framework demonstrated strong predictive accuracy for kinetic indicators (adjusted
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.