Making Nanomaterials Safer by Design?

Schwarz-Plaschg, Claudia; Kallhoff, Angela; Eisenberger, Iris

doi:10.1007/s11569-017-0307-4

Cited by 46 publications

(31 citation statements)

References 10 publications

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“…In the context of regulation, it is difficult to implement this approach as current testing paradigms identify risk late into the production process, slowing down innovation and increasing costs. To address this, one of the proposed approaches, safe(r)-by-design (SbD), aims to incorporate hazard assessment into the design process of novel MNMs (Schwarz-plaschg et al, 2017). The application of SbD to MNMs was developed in the European FP7 projects NANoREG and Prosafe and is being expanded on in the European Horizon 2020 project NanoReg2.…”

Section: Introductionmentioning

confidence: 99%

Investigating a transcriptomic approach on marine mussel hemocytes exposed to carbon nanofibers: An in vitro/in vivo comparison

et al. 2019

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Manufactured nanomaterials are an ideal test case of the precautionary principle due to their novelty and potential environmental release. In the context of regulation, it is difficult to implement for manufactured nanomaterials as current testing paradigms identify risk late into the production process, slowing down innovation and increasing costs. One proposed concept, namely safe(r)-by-design, is to incorporate risk and hazard assessment into the design process of novel manufactured nanomaterials by identifying risks early. When investigating the manufacturing process for nanomaterials, differences between products will be very similar along key physicochemical properties and biological endpoints at the individual level may not be sensitive enough to detect differences whereas lower levels of biological organization may be able to detect these variations. In this sense, the present study used a transcriptomic approach on Mytilus edulis hemocytes following an in vitro and in vivo exposure to three carbon nanofibers created using different production methods. Integrative modeling was used to identify if gene expression could be in linked to physicochemical features. The results suggested that gene expression was more strongly associated with the carbon structure of the nanofibers than chemical purity. With respect to the in vitro/in vivo relationship, results suggested an inverse relationship in how the physicochemical impact gene expression.

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Section: Introductionmentioning

confidence: 99%

Investigating a transcriptomic approach on marine mussel hemocytes exposed to carbon nanofibers: An in vitro/in vivo comparison

et al. 2019

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“…If two regression models have similar RMSE, F-values (the ratio between explained and unexplained variance) and P-values (the probability of finding the observed or more extreme results) can help determine the model of choice [22,129]. Robustness metrics such as squared cross validated correlation coefficient (Q 2 ), leave-one-out cross-validation coefficient (Q 2 LOO ), and leave-many-out cross-validation coefficients (Q 2 LMO−10% and Q 2 LMO−25% ) are popular robustness indicators [46,47]. To avoid the possibility of overestimation by using only leave-one-out cross validation, a bootstrap procedure (Q 2 Boot ) is suggested [23] and is mainly suitable for a limited number of training cases [50].…”

Section: Robustnessmentioning

confidence: 99%

“…The leave-many-out approach remove a different number of values from the data set (10%, 20%, 25%, or 50%), depending on the size of the dataset even though there is no rule-of-thumb as to the percentages one should apply for cross validation or data split. Besides Q 2 LOO , the root-mean square error of cross-validation (R 2 CV ) can be calculated [38,94]. The minimum criteria for a successful QSAR model is R 2 ≥ 0.6 and Q 2 LMO of ≥ 0.5 [84], whereas training and the test set R 2 value difference should not exceed 0.3 [56].…”

Section: Robustnessmentioning

confidence: 99%

“…Similarly, the "true" model can be characterized by calculating the values of RMSE and RMSE CV [34]. Monte Carlo can also be used, whereby the dependent variable is randomized and the models rerun [22], as well as ensuring model's Q 2 CV statistically significance value [54], the CCR acceptance thresholds [76], or its prediction accuracy [30]. QUIK (Q under influence of K) rule [28], which is a basic criterion that optimizes the ranking of the best features combinations, enables high predictor collinearity models to be rejected [40].…”

Section: Chance Testingmentioning

confidence: 99%

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Practices and Trends of Machine Learning Application in Nanotoxicology

et al. 2020

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Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.

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“…They aim at reducing adverse effects on human health and the environment by altering nanoproduct design (Soeteman-Hernandez et al, 2019) and by ensuring safety along its lifecycle (Bottero et al, 2017;Kraegeloh et al, 2018). The SbD concept is therefore different from conventional risk assessment approaches, which only consider safety when the product is already fully developed (Schwarz-Plaschg et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

A Methodological Safe-by-Design Approach for the Development of Nanomedicines

Schmutz

Borges

Jesus

et al. 2020

Front. Bioeng. Biotechnol.

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Safe-by-Design (SbD) concepts foresee the risk identification and reduction as well as uncertainties regarding human health and environmental safety in early stages of product development. The EU's NANoREG project and further on the H2020 ProSafe initiative, NanoReg2, and CALIBRATE projects have developed a general SbD approach for nanotechnologies (e.g., paints, textiles, etc.). Based on it, the GoNanoBioMat project elaborated a methodological SbD approach (GoNanoBioMat SbD approach) for nanomedicines with a focus on polymeric nanobiomaterials (NBMs) used for drug delivery. NBMs have various advantages such as the potential to increase drug efficacy and bioavailability. However, the nanoscale brings new challenges to product design, manufacturing, and handling. Nanomedicines are costly and require the combination of knowledge from several fields. In this paper, we present the GoNanoBioMat SbD approach, which allows identifying and addressing the relevant safety aspects to address when developing polymeric NBMs during design, characterization, assessment of human health and environmental risk, manufacturing and handling, and combines the nanoscale and medicine field under one approach. Furthermore, regulatory requirements are integrated into the innovation process.

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Making Nanomaterials Safer by Design?

Cited by 46 publications

References 10 publications

Investigating a transcriptomic approach on marine mussel hemocytes exposed to carbon nanofibers: An in vitro/in vivo comparison

Investigating a transcriptomic approach on marine mussel hemocytes exposed to carbon nanofibers: An in vitro/in vivo comparison

Practices and Trends of Machine Learning Application in Nanotoxicology

A Methodological Safe-by-Design Approach for the Development of Nanomedicines

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