Data-Driven Quantitative Intrinsic Hazard Criteria for Nanoproduct Development in a Safe-by-Design Paradigm: A Case Study of Silver Nanoforms

The current European (EU) policies, that is, the Green
Deal, envisage
safe and sustainable practices for chemicals, which include nanoforms
(NFs), at the earliest stages of innovation. A theoretically safe
and sustainable by design (SSbD) framework has been established from
EU collaborative efforts toward the definition of quantitative criteria
in each SSbD dimension, namely, the human and environmental safety
dimension and the environmental, social, and economic sustainability
dimensions. In this study, we target the safety dimension, and we
demonstrate the journey toward quantitative intrinsic hazard criteria
derived from findable, accessible, interoperable, and reusable data.
Data were curated and merged for the development of new approach methodologies,
that is, quantitative structure–activity relationship models
based on regression and classification machine learning algorithms,
with the intent to predict a hazard class. The models utilize system
(i.e., hydrodynamic size and polydispersity index) and non-system
(i.e., elemental composition and core size)-dependent nanoscale features
in combination with biological in vitro attributes and experimental
conditions for various silver NFs, functional antimicrobial textiles,
and cosmetics applications. In a second step, interpretable rules
(criteria) followed by a certainty factor were obtained by exploiting
a Bayesian network structure crafted by expert reasoning. The probabilistic
model shows a predictive capability of ≈78% (average accuracy
across all hazard classes). In this work, we show how we shifted from
the conceptualization of the SSbD framework toward the realistic implementation
with pragmatic instances. This study reveals (i) quantitative intrinsic
hazard criteria to be considered in the safety aspects during synthesis
stage, (ii) the challenges within, and (iii) the future directions
for the generation and distillation of such criteria that can feed
SSbD paradigms. Specifically, the criteria can guide material engineers
to synthesize NFs that are inherently safer from alternative nanoformulations,
at the earliest stages of innovation, while the models enable a fast
and cost-efficient in silico toxicological screening of previously
synthesized and hypothetical scenarios of yet-to-be synthesized NFs.