Felice Carlo Simeone

The safety-by-design approach is a decision process that aims to minimize the risks of nanoforms in a context of high uncertainty.

The SAbyNA platform supports value chain actors in developing safe-by-design nano-enabled products early in their development phases. The potential of digital guidance is exemplified in a case study on carbon nanotube-based 3D-printed products.

Dopants and coatings regulate the redox profile of magnetite-, Ag-, and HA-hybrid NBMs. Created in BioRender. Alcolea-Rodriguez, V. (2025) https://BioRender.com/fm1uwvb.

In chemico ENM reactivity assays quantify oxidative rates and reactive surface sites, refining dose metrics for nanotoxicity assessment and enabling safe-by-design nanomaterials.

An effective, eco-friendly and easily scalable nanosilver-based technology offers affordable and broad-spectrum antimicrobial solutions against SARS-CoV-2 and Escherichia coli .

Fitting theoretical models to experimental data for dose–response screenings of nanoparticles yields values of several hazard metrics that can support risk management. In this paper, we describe a Bayesian approach to the analysis of dose–response data for nanoparticles that takes into account multiple sources of uncertainty. Specifically, we develop a Bayesian model for the analysis of data for the cytotoxicity of ZnO nanoparticles that follow the log-logistic equation. This model reproduces the unequal variance across doses observed in the experimental data, incorporates information about the sensitivity of the cytotoxicity assay used (i.e. resazurin), and complements experimental data with historical information about the system. The model determines probability distributions for multiple values of toxicity potency (EC₅₀), and exponential decay (the slope s); these distributions provide a direct measure of uncertainty in terms of probabilistic credibility intervals. By substituting these distributions in the log-logistic equation, we determine upper and lower limits of the benchmark dose (BMD), corresponding to upper and lower limits of credibility intervals with 95% probability given the experimental data, multiple sources of uncertainty, and historical information. In view of a reduction of costs and time of dose–response screenings, we use the Bayesian model for the cytotoxicity of ZnO nanoparticles to identify the experimental design that uses the minimum number of data while reducing uncertainty in the estimation of both fitting parameters and BMD.

Due to their sizes, nanoparticles can penetrate into biological systems easily; anticipating their harmful interactions at the cellular level makes it possible to safeguard workers, consumers, and the environment effectively. Decades of research have identified key properties of nanomaterials that induce adverse responses, and this evidence suggests that the toxicity of a nanoparticle can be inferred from its abiotic behavior. Experimental physical–chemical characterization of nanoparticles, however, is complex and time consuming, and the absence of any standardized method has generated controversial results. This paper shows how known adverse modes of action of nanoparticles arise from fundamental physical–chemical parameters that don't need any experimental quantification. We show that cytotoxicity of metal oxides in different types of in vitro systems, which include E. coli, rat alveolar macrophages, human bronchial epithelial cells, Daphnia magna, and Aliivibrio fischeri, can be foreseen on the basis of the values of oxidation number (Z) and ionic potential (IP) of the cation, and surface reducibility (SR), and redox reactivity (RR) of the oxide. Importantly, the values of these fundamental physical–chemical parameters can be easily deduced from the chemical formula of the nanoparticle with the help of a periodic table. Combining these parameters in a naïve Bayes classifier, a robust probabilistic model that can be run on a pocket calculator, makes it possible to determine the most probable level of toxicity of a nanoparticle given its composition. Results indicate that the probability that nano-oxides exhibit very high cytotoxicity (EC₅₀ < 10⁻³ mol L⁻¹) decreases with increasing oxidation number Z of the cation; high values of Z, however, may become unstable and activate adverse redox processes; in contrast, stable, redox-inert reducible oxides tend to be, probabilistically, less toxic than oxidizable ones.

Hazard identification is the key step in risk assessment and management of manufactured nanomaterials (NM). However, the rapid commercialisation of nano-enabled products continues to out-pace the development of a prudent risk management mechanism that is widely accepted by the scientific community and enforced by regulators. However, a growing body of academic literature is developing promising quantitative methods. Two approaches have gained significant currency. Bayesian networks (BN) are a probabilistic, machine learning approach while the weight of evidence (WoE) statistical framework is based on expert elicitation. This comparative study investigates the efficacy of quantitative WoE and Bayesian methodologies in ranking the potential hazard of metal and metal-oxide NMs—TiO₂, Ag, and ZnO. This research finds that hazard ranking is consistent for both risk assessment approaches. The BN and WoE models both utilize physico-chemical, toxicological, and study type data to infer the hazard potential. The BN exhibits more stability when the models are perturbed with new data. The BN has the significant advantage of self-learning with new data; however, this assumes all input data is equally valid. This research finds that a combination of WoE that would rank input data along with the BN is the optimal hazard assessment framework.