ABOUT QLmaterials
For Innovative Advanced Materials (IAMs) including Engineered Nanomaterials (ENM), even small changes in structure or surface properties can create new variants with different safety profiles. Testing every possible version in the lab can be costly and often unrealistic, especially when you need to compare many design options.
ABOUT QLmaterials
For Innovative Advanced Materials (IAMs) including Engineered Nanomaterials (ENM), even small changes in structure or surface properties can create new variants with different safety profiles. Testing every possible version in the lab can be costly and often unrealistic, especially when you need to compare many design options.
QLmaterials is a safety app for IAMs (including ENM). It uses machine-learning-based models built on curated experimental data to predict human-relevant toxicity endpoints. This lets you screen and prioritise material variants, focus laboratory testing where it is most informative, reduce costs and support safety by design decisions.
Use predictions to identify
which (nano)material variants
need experimental follow up
with token-based payment
- no subscription or
software download needed
regulatory-relevant endpoints
for better understanding the
safety profile of IAMs
Explore “what if” changes
digitally and move towards
safer-by-design IAMs
predictions based on
properties that matter for
advanced (nano)materials
Cut the number
of (nano)material specific studies
and shorten decision-making
Who is it for?
R&D teams
when you need a view on potential toxicity of new IAM before committing to full testing
Toxicologists and safety assessors
when you have to compare multiple IAMs options
and build a safety argument
Regulatory and registration teams
when you prepare documentation and want in silico evidence to support your testing strategies
Innovation and SSbD teams
when you need to explore design changes to bring
safety into materials development
01
Choose and endpoint &
specify the cellular model
Regulatory-relevant endpoints include:
mutagenicity,
genotoxicity,
pro-inflammatory responses,
for cellular models such as:
02
Define material & run prediction
Set different physicochemical parameters of a (nano) material such as particle size distribution, shape and crystal structure.
Only one click is required to make the prediction – no specialized chemoinformatics knowledge is needed.
03
Generate registration supporting report
Generate a tailored report aligned with relevant regulatory requirements, so you can directly use it in your safety documentation.
The report includes:
a detailed summary of defined material,
prediction result, description and scope
of its reliability, methodology used,
QSAR Prediction Reporting Format standard in line with OECD ENV/CBC/MONO(2023)32 guidance.
