Bayesian based similarity assessment of nanomaterials to inform grouping

Tsiliki, Georgia; Şeleci, Didem Ağ; Zabeo, Alex; Basei, Gianpietro; Hristozov, Danail; Jeliazkova, Nina; Boyles, Matthew; Murphy, Fiona; Peijnenburg, Willie J.G.M.; Wohlleben, Wendel; Stone, Vicki

doi:10.1016/j.impact.2022.100389

Cited by 9 publications

(14 citation statements)

References 26 publications

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“…To demonstrate the methodology and the R script, they were tested in a proof-of-concept exercise against a literature-based dataset for immobilization. The method was also validated against other methods in (Jeliazkova et al, 2021) in this same issue, results were also consistent with conclusions from other methods applied to the same data in the GRACIOUS project (Tsiliki et al, 2021). This confirmed the validity and the soundness of the proposed approach.…”

Section: Discussionsupporting

confidence: 78%

“…To support the grouping, GRACIOUS has also proposed an array of methods to assess similarity between NFs in terms of intrinsic and extrinsic physicochemical characteristics as well as toxicity, either via a pairwise analysis conducted property-by-property, or by assessing all relevant properties and hazard endpoints simultaneously via multidimensional analysis (Jeliazkova et al, 2021). Such methods are based for example on an x-fold comparison (Janer et al, 2021), Euclidean distance, Bayesian logic (Tsiliki et al, 2021) or clustering methods (Jeliazkova et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ordered weighted average based grouping of nanomaterials with Arsinh and dose response similarity models

Zabeo¹,

Basei²,

Tsiliki

et al. 2022

NanoImpact

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Section: Discussionsupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Ordered weighted average based grouping of nanomaterials with Arsinh and dose response similarity models

Zabeo¹,

Basei²,

Tsiliki

et al. 2022

NanoImpact

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“…2 B). The values presented are scaled between 0 and 1, where 1 indicates identical curves, while values above or equal to 0.7 indicate highly similar NFs [ 26 ]. The yellow and light orange colors (from light orange: 0.7 to yellow: 0.5) represent the comparison of mesoporous NFs to the other NFs suggesting that these silica NFs cannot be considered as a single group i.e., they are not highly similar according to the BF analysis but as two groups.…”

Section: Resultsmentioning

confidence: 99%

“…Another aim was to apply different methods for assessing similarity to support a quantitative similarity assessment. The details of these methods are described elsewhere [ 9 , 26 ] but were applied here to demonstrate their usefulness. The data provided by the case study allowed for the testing of two approaches the BF and the clustering analyses.…”

Section: Resultsmentioning

confidence: 99%

“…Different approaches to assess similarity across the DNs (dissolution and hazard) were used: Stochastic likelihood-based BF approach that assesses probabilistically two different properties of the different NFs simultaneously (for more details refer to supplementary information)[ 26 ]. This tool was applied on both dissolution and hazard DNs.…”

Section: Methodsmentioning

confidence: 99%

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Grouping of orally ingested silica nanomaterials via use of an integrated approach to testing and assessment to streamline risk assessment

Cristo

Ude

Tsiliki³

et al. 2022

Part Fibre Toxicol

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Background Nanomaterials can exist in different nanoforms (NFs). Their grouping may be supported by the formulation of hypotheses which can be interrogated via integrated approaches to testing and assessment (IATA). IATAs are decision trees that guide the user through tiered testing strategies (TTS) to collect the required evidence needed to accept or reject a grouping hypothesis. In the present paper, we investigated the applicability of IATAs for ingested NFs using a case study that includes different silicon dioxide, SiO2 NFs. Two oral grouping hypotheses addressing local and systemic toxicity were identified relevant for the grouping of these NFs and verified through the application of oral IATAs. Following different Tier 1 and/or Tier 2 in vitro methods of the TTS (i.e., in vitro dissolution, barrier integrity and inflammation assays), we generated the NF datasets. Furthermore, similarity algorithms (e.g., Bayesian method and Cluster analysis) were utilized to identify similarities among the NFs and establish a provisional group(s). The grouping based on Tier 1 and/or Tier 2 testing was analyzed in relation to available Tier 3 in vivo data in order to verify if the read-across was possible and therefore support a grouping decision. Results The measurement of the dissolution rate of the silica NFs in the oro-gastrointestinal tract and in the lysosome identified them as gradually dissolving and biopersistent NFs. For the local toxicity to intestinal epithelium (e.g. cytotoxicity, membrane integrity and inflammation), the biological results of the gastrointestinal tract models indicate that all of the silica NFs were similar with respect to the lack of local toxicity and, therefore, belong to the same group; in vivo data (although limited) confirmed the lack of local toxicity of NFs. For systemic toxicity, Tier 1 data did not identify similarity across the NFs, with results across different decision nodes being inconsistent in providing homogeneous group(s). Moreover, the available Tier 3 in vivo data were also insufficient to support decisions based upon the obtained in vitro results and relating to the toxicity of the tested NFs. Conclusions The information generated by the tested oral IATAs can be effectively used for similarity assessment to support a grouping decision upon the application of a hypothesis related to toxicity in the gastrointestinal tract. The IATAs facilitated a structured data analysis and, by means of the expert’s interpretation, supported read-across with the available in vivo data. The IATAs also supported the users in decision making, for example, reducing the testing when the grouping was well supported by the evidence and/or moving forward to advanced testing (e.g., the use of more suitable cellular models or chronic exposure) to improve the confidence level of the data and obtain more focused information.

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Redox in Cancer Metabolism: Manipulation of Cancer Metabolism Through Exploitation of Redox Environments

McBeth

2023

ChemElectroChem

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The use of bioelectronics is a quickly evolving and developing field offering novel application to solve problems across numerous biological disciplines. Cancer research has focussed on various biomarkers as a means for detection and for potential therapeutic treatments. Several therapies and medications focus on influencing redox state within the cellular environment, with some of these therapies utilising cyclic redox components, allowing for repeated oxidation/reduction at target sites. This offers great potential for electrical (DC and AC) and electrochemical control over the intracellular redox environment. Herein explores the redox circuits in cancer and its metabolism, the influence of mediated oxidative stress in redox altered environments, therapeutics that target specific redox pathways and their redox active proteins, and the implementation of electrochemical interrogation to control the cellular redox environment to manipulate cancer metabolism.

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Bayesian based similarity assessment of nanomaterials to inform grouping

Cited by 9 publications

References 26 publications

Ordered weighted average based grouping of nanomaterials with Arsinh and dose response similarity models

Ordered weighted average based grouping of nanomaterials with Arsinh and dose response similarity models

Grouping of orally ingested silica nanomaterials via use of an integrated approach to testing and assessment to streamline risk assessment

Redox in Cancer Metabolism: Manipulation of Cancer Metabolism Through Exploitation of Redox Environments

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