Relating Nanoparticle Properties to Biological Outcomes in Exposure Escalation Experiments

Quantifying uncertainty in dose-response screenings of nanoparticles: a Bayesian data analysis

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.

Nano-cell and nano-pollutant interactions constitute key elements in nanoparticle-pollutant combined cytotoxicity

As the wide application of carbon nanoparticles (CNPs) and zinc oxide nanoparticles (ZnONPs), as well as ubiquitous chromium (Cr(VI)) pollution in environment, the chance of human exposure to CNPs/ZnONPs and their Cr(VI) adducts is enhanced. We therefore investigated the impacts of nano-cell and nano-Cr(VI) interactions on nanoparticle-Cr(VI) combined cytotoxicity in human lung epithelial (A549) cells. Our results showed that nano-cell and nano-pollutant interactions were the key elements in NP-pollutant combined cytotoxicity, as determined by cell death, oxidative stress and mitochondrial dysfunction. A strong adsorption of Cr(VI) on CNPs and reduction of Cr(VI) to Cr(III) were confirmed, resulting in the reduced cytotoxicity of CNP-Cr(VI) adducts. In contrast, ZnONPs caused the destruction of cell membranes so that more ZnONP-Cr(VI) adducts could enter the cells. Meantime, more Cr contents could be released from ZnONP-Cr(VI) adducts once entering cells and locating in lysosomes than that from CNP-Cr(VI) adducts. These two reasons together caused the enhanced cytotoxicity of ZnONP-Cr(VI) adducts. These findings indicate that the in-depth investigations on the interaction mechanisms are crucial to comprehensively understand the combined cytotoxicity of different NPs and pollutants.

The Role of Machine Learning in Centralized Authorization Process of Nanomedicines in European Union

BACKGROUND

Machine Learning (ML) has experienced an increasing use, given the possibilities to expand the scientific knowledge of different disciplines, such as nanotechnology. This has allowed the creation of Cheminformatic models capable of predicting biological activity and physicochemical characteristics of new components with high success rates in training and test partitions. Given the current gaps of scientific knowledge and the need for efficient application of medicines products law, this paper analyzes the position of regulators for marketing medicinal nanoproducts in the European Union and the role of ML in the authorization process.

METHODS

In terms of methodology, a dogmatic study of the European regulation and the guidance of the European Medicine Agency on the use of predictive models for nanomaterials was carried out. The study has, as the framework of reference, the European Regulation 726/2004 and has focused on the analysis of how ML processes are contemplated in the regulations.

RESULTS

As a result, we present a discussion of the information that must be provided for every case for simulation methods. The results show a favorable and flexible position for the development of the use of predictive models to complement the applicant's information.

CONCLUSION

It is concluded that Machine Learning has the capacity to help improve the application of nanotechnology medicine products regulation. Future regulations should promote this kind of information given the advanced state of the art in terms of algorithms that are able to build accurate predictive models. This especially applies to methods, such as Perturbation Theory Machine Learning (PTML), given that it is aligned with principles promoted by the standards of Organization for Economic Co-operation and Development (OECD), European Union regulations, and European Authority Medicine. To our best knowledge, this is the first study focused on nanotechnology medicine products and machine learning used to support technical European public assessment reports (EPAR) for complementary information.

Nanotoxicology data for in silico tools: a literature review

The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models. Instead of seeking mechanistic interpretations this review maps the pathways followed, involving biological features in relation to NPs exposure, their physico-chemical characteristics and the most commonly predicted outcomes. The results, derived from published research of the last decade, are summarized visually, providing prior-based data mining paradigms to be readily used by the nanotoxicology community in computational studies.

Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials

Although nanotechnology is a new and rapidly growing area of science, the impact of nanomaterials on living organisms is unknown in many aspects. In this regard, it is extremely important to perform toxicological tests, but complete characterization of all varying preparations is extremely laborious. The computational technique called quantitative structure–activity relationship, or QSAR, allows reducing the cost of time- and resource-consuming nanotoxicity tests. In this review, (Q)SAR cytotoxicity studies of the past decade are systematically considered. We regard here five classes of engineered nanomaterials (ENMs): Metal oxides, metal-containing nanoparticles, multi-walled carbon nanotubes, fullerenes, and silica nanoparticles. Some studies reveal that QSAR models are better than classification SAR models, while other reports conclude that SAR is more precise than QSAR. The quasi-QSAR method appears to be the most promising tool, as it allows accurately taking experimental conditions into account. However, experimental artifacts are a major concern in this case.

Quasi-SMILES and nano-QFAR: united model for mutagenicity of fullerene and MWCNT under different conditions

Simplified molecular input-line entry system (SMILES) are a tool to represent molecular features of various compounds. Quasi-SMILES is a tool to represent various eclectic features of interaction between complex substances and bio targets (cells, organs, organisms). The construction and the application of quasi-SMILES in order to build up a model for prediction of mutagenicity of fullerene and multi-walled carbon-nanotubes (MWCNTs) are described in this work: instead of paradigm "endpoint is a mathematical function of molecular structure", the paradigm "endpoint is a mathematical function of eclectic data (features)" is used.

Optimal nano-descriptors as translators of eclectic data into prediction of the cell membrane damage by means of nano metal-oxides

Systematization of knowledge on nanomaterials has become a necessity with the fast growth of applications of these species. Building up predictive models that describe properties (both beneficial and hazardous) of nanomaterials is vital for computational sciences. Classic quantitative structure-property/activity relationships (QSPR/QSAR) are not suitable for investigating nanomaterials because of the complexity of their molecular architecture. However, some characteristics such as size, concentration, and exposure time can influence endpoints (beneficial or hazardous) related to nanoparticles and they can therefore be involved in building a model. Application of the optimal descriptors calculated with the so-called correlation weights of various concentrations and different exposure times are suggested in order to build up a predictive model for cell membrane damage caused by a series of nano metal-oxides. The numerical data on correlation weights are calculated by the Monte Carlo method. The obtained results are in good agreement with the experimental data.

Pulmonary toxicity of nanomaterials: a critical comparison of published in vitro assays and in vivo inhalation or instillation studies

To date, guidance on how to incorporate in vitro assays into integrated approaches for testing and assessment of nanomaterials is unavailable. In addressing this shortage, this review compares data from in vitro studies to results from in vivo inhalation or intratracheal instillation studies. Globular nanomaterials (ion-shedding silver and zinc oxide, poorly soluble titanium dioxide and cerium dioxide, and partly soluble amorphous silicon dioxide) and nanomaterials with higher aspect ratios (multiwalled carbon nanotubes) were assessed focusing on the Organisation for Economic Co-Operation and Development (OECD) reference nanomaterials for these substances. If in vitro assays are performed with dosages that reflect effective in vivo dosages, the mechanisms of nanomaterial toxicity can be assessed. In early tiers of integrated approaches for testing and assessment, knowledge on mechanisms of toxicity serves to group nanomaterials thereby reducing the need for animal testing.

Regenerative nanomedicine: current perspectives and future directions

Nanotechnology has considerably accelerated the growth of regenerative medicine in recent years. Application of nanotechnology in regenerative medicine has revolutionized the designing of grafts and scaffolds which has resulted in new grafts/scaffold systems having significantly enhanced cellular and tissue regenerative properties. Since the cell–cell and cell-matrix interaction in biological systems takes place at the nanoscale level, the application of nanotechnology gives an edge in modifying the cellular function and/or matrix function in a more desired way to mimic the native tissue/organ. In this review, we focus on the nanotechnology-based recent advances and trends in regenerative medicine and discussed under individual organ systems including bone, cartilage, nerve, skin, teeth, myocardium, liver and eye. Recent studies that are related to the design of various types of nanostructured scaffolds and incorporation of nanomaterials into the matrices are reported. We have also documented reports where these materials and matrices have been compared for their better biocompatibility and efficacy in supporting the damaged tissue. In addition to the recent developments, future directions and possible challenges in translating the findings from bench to bedside are outlined.