Study-Grouped Predictive Toxicology of In Vitro Polystyrene Micro- and Nanoplastic Cytotoxicity: Geometry-Derived Administered-Dose Metrics and a Nanoscale-Restricted Sensitivity Analysis
Polymers, cilt.18, sa.20, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 18 Sayı: 20
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/polym18202575
- Dergi Adı: Polymers
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chemical Abstracts Core, Compendex, INSPEC, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: cytotoxicity, dose metrics, machine learning, microplastics, nanoplastics, particle geometry, polystyrene, predictive toxicology, study-grouped validation, surface properties
- Lokman Hekim Üniversitesi Adresli: Evet
Özet
Polystyrene (PS) micro- and nanoplastics show heterogeneous in vitro cytotoxicity across particle characteristics and exposure conditions. We examined whether geometry-derived administered-dose metrics improve out-of-study prediction of in vitro cytotoxicity and whether ≤100 nm restriction improves performance independently of study composition. From the 2443 literature-derived observations across 98 mammalian in vitro studies, 1701 observations from 82 studies formed the primary mass-concentration dataset and 995 from 60 studies formed the ≤100 nm subset. Random forests were evaluated using nested study-grouped cross-validation with inverse-study-frequency weights. A matched dataset of 141 observations from six studies enabled leave-one-article-out comparisons of mass, particle-number, and geometric surface-area metrics. In the nanoscale subset, observation-level mean absolute error decreased from 15.36 to 14.37 percentage points and R2 increased from 0.283 to 0.447, whereas study-balanced mean absolute error did not improve (15.37 versus 15.60). In a composition-controlled 22-study analysis evaluated on identical nanoscale test observations, nanoscale-only training showed no clear advantage. Ridge regression favored mass concentration, while random forest showed a small, uncertain particle-number advantage. Geometry-derived administered-dose metrics offered no consistent model-independent advantage. Study-mean R2 (0.095) indicates limited generalizability. Mass concentration remains a practical reference within this evidence base; standardized reporting is needed, and geometry-derived metrics reflect administered, not delivered, dose.