Bridging the gaps in nanosafety for animal-free prediction of adverse outcomes

Informazioni relative al progetto

nanoPASS

ID dell’accordo di sovvenzione: 101092741

DOI

10.3030/101092741

Data della firma CE 24 Novembre 2022

Data di avvio 1 Gennaio 2023

Data di completamento 31 Dicembre 2026

Finanziato da

• Digital, Industry and Space

Costo totale

€ 3 073 736,25

Contributo UE

€ 3 073 736,25

Coordinato da

INSTITUT JOZEF STEFAN
Slovenia

Periodic Reporting for period 2 - nanoPASS (Bridging the gaps in nanosafety for animal-free prediction of adverse outcomes)

Periodo di rendicontazione: 2024-07-01 al 2025-12-31

Sintesi del contesto e degli obiettivi generali del progetto

nanoPASS develops cost-effective, animal-free predictions of long-term adverse outcomes (AOs) of inhaled nanomaterials that can be demonstrated to work in regulatory and industrial settings.

To this end, we are shifting the focus of nanosafety testing from late endpoints to early key events (KEs) leading to adverse outcomes (AOs). As such tests can only be based on mechanistic understanding, we need to close the knowledge gaps and match KEs in vitro and in vivo. The key bridging methods provided by nanoPASS consortium are intravital in vivo microscopy, quantitative time-lapse in vitro microscopies, and automated identification of the modes of action (i.e. KE relationships) with proprietary in silico algorithms, supported by datamining of the worlds’ largest in vivo database and single-cell omics data, and computational modelling of structure-function relationships.

We are implementing this approach in the context of 4 AOs related to inhalation of nanomaterial: reduced lung function (AOP302), cardiovascular disease (AOP237), chronic inflammation (KE in AOP173, fibrosis; renumbered AOP33 after recent endorsement), and neurodegeneration (no AOP proposed), to which we have added cancer (AOP451) beyond the proposal.

The developed in vitro/in silico prediction models are being calibrated against in vivo data with 40+ benchmark materials, and validated with real-life materials at different stages of the life cycle from 5 diverse industrial cases: construction, electronics waste, advanced materials for catalysis, nanoplastics, and medical materials.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

We have finalised calibration and pre-validation of the infinite in vitro-learned digital twin as a New Approach Methodology (NAM) to predict chronic lung inflammation against in vivo data for 44 benchmark materials.
• It delivers quantitative, regulatory-relevant threshold doses (LOAEL) across 3 orders of magnitude with 94% accuracy.
• As it does not require physico-chemical characterisation, it applies to real-world materials, demonstrated with the first industrial case.
• Applications have been submitted to ECVAM and fNIH, and collaborations established with ECHA and BfR/EFSA toward formal adoption in four contexts of use.
• With results in under a week, it addresses the need for time- and cost-efficient long-term hazard assessment.

Additional key achievements:
• We contributed to endorsement of the first AOP for (nano)materials (AOP173 – pulmonary fibrosis) and submitted AOP237 (atherosclerosis), which passed OECD internal review and is entering scientific review.
• The acellular lung surfactant model (Reduced lung function, AOP302) is nearing completion.
• We developed the first environmentally triggered in vitro neurodegeneration model reproducing the three cell-level hallmarks of Alzheimer’s disease (Nanotoxicology 2024).
• Identified in vivo-relevant key event candidates linked to cell division defects in vitro (Nature Communications 2024), expanding impact to cancer (AOP451).
• In the ToxAtlas of carbon-based nanomaterials (ACS Nano 2025), we described initiating cell circuits of pulmonary inflammation and showed distinct MoAs driving acute lung inflammation.
• We integrated multi-timepoint in vivo transcriptomics to define conserved and material-specific key events linking early pulmonary activation to chronic immune dysregulation and fibrosis after crystalline silica exposure.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

We are developing cost-effective, animal-free predictions of four inhalation-related AOs: reduced lung function (AOP302), cardiovascular disease (AOP237), chronic inflammation/fibrosis (AOP173; linked to lung cancer, AOP451), and neurodegeneration (no AOP yet).

To this end, we pursue the following objectives (O):

(O1) Develop in vitro models predictive of in vivo AOs
Using combined bottom-up (in vitro) and top-down (in vivo) approaches, we identified mechanistically relevant bridging events.
• Ten of the identified bridging KEs are already in AOPWiki listed as inflammation-related KEs, as is the predicted endpoint – neutrophils influx (KE1497).
• At least six early events were independently confirmed by partners or collaborators.
• Demonstrated that different nanomaterials trigger acute lung inflammation via distinct MoAs.

(O2) Track KE dynamics
We integrated high-throughput, time-lapse microscopy with label-free nanomaterial localisation at two partners. A confocal fluorescence–Stimulated Raman Scattering platform was established for in vitro chemical fingerprinting and material characterisation in complex environments.

(O3) Develop in silico models for quantitative AO prediction
Our in vitro/in silico translation model incorporates new events (demonstrated for 17) to predict multiple AOs. By mapping relationships among early events, the model enables material-specific triggering of material-agnostic outcomes. We identified new descriptors for complex materials, advanced algorithms for predicting nanomaterial–biomolecule interactions, and developed methods to predict gene expression patterns.

(O4) Calibrate and validate models with in vivo data
We compiled in vivo datasets for 44 benchmark materials (including JRC) and grounded predictions in mechanistic AOP knowledge. For industrial validation, we sampled five material families at high-exposure life-cycle stages: cement (and intermediates), e-waste, advanced bimetallic nanoparticles, degraded 3D-printed plastics, and dental fillings. We characterised their physico-chemical properties, completed most planned in vitro/in vivo studies, and conducted personal exposure measurements for risk assessment and mitigation at one site.