Scientific Opinion on Flavouring Group Evaluation 75, Revision 1 (FGE.75Rev1): Consideration of tetrahydrofuran derivatives evaluated by JECFA (63rd meeting) structurally related to tetrahydrofuran derivatives evaluated by EFSA in FGE.33 (2008)

Flavourings,

doi:10.2903/j.efsa.2016.4335

Cited by 3 publications

(1 citation statement)

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“…Application of the Procedure to Aliphatic, Alicyclic and Aromatic Saturated and Unsaturated Tertiary Alcohols, Aromatic Tertiary Alcohols and their Esters evaluated by EFSA (EFSA, 2015) Table 3: Specification Summary of the Substances in the JECFA Flavouring Group of Aliphatic, Acyclic and Alicyclic Terpenoid Tertiary Alcohols and Structurally Related Substances (JECFA, 2008b) 18 Table 4: Genotoxicity Data (in vitro) EFSA / FGE.18Rev2 (EFSA, 2011). Substances listed in brackets are the JECFA evaluated supporting substances in FGE.18Rev2 ........................................... 19 Table 5: Genotoxicity Data (in vivo) for 18Rev2 (EFSA, 2011).…”

Section: 2mentioning

confidence: 99%

Scientific Opinion on Flavouring Group Evaluation 90, Revision 1 (FGE.90Rev1): consideration of six substances evaluated by JECFA (68th meeting) structurally related to aliphatic, alicyclic and aromatic saturated and unsaturated tertiary alcohols, aromatic tertiary alcohols and their esters evaluated by EFSA in FGE.18Rev1 and FGE.75Rev1

Flavourings¹

2016

EFS2

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The Panel on Food Contact Materials, Enzymes, Flavourings and Processing Aids of EFSA was requested to consider evaluations of flavouring substances assessed since 2000 by the Joint FAO/WHO Expert Committee on Food Additives (the JECFA), and to decide whether further evaluation is necessary, as laid down in Commission Regulation (EC) No 1565/2000. The present consideration concerns a group of six aliphatic, acyclic and alicyclic terpenoid tertiary alcohols and structurally related substances evaluated by JECFA at the 68th meeting in 2007. This revision of FGE.90 is made because additional toxicity data have become available for a structurally related substance in FGE.75Rev1, anhydrolinalool oxide (5) . The Panel agrees with the application of the Procedure as performed by the JECFA for four substances 02.245, 02.250 and 02.251]. For two substances .076 and 13.087] it could not be concluded that they are metabolised to innocuous substances. Based on the exposure estimates (MSDI) and the no observed adverse effect level (NOAEL) from 90-day toxicity study with anhydrolinalool oxide (5), the Panel considered that the substances [FL-no: 13.076 and 13.087] were not of safety concern at the estimated levels of intake based on the maximised survey-derived daily intake (MSDI) approach. The specifications for the materials of commerce have also been considered. For substance ] information on the stereoisomeric composition has not been specified, and for substance .087], the identity of the isomers needs to be specified. For three substances 13.076 and 13.087] the modified theoretical added maximum daily intake (mTAMDI) is above the threshold of concern and therefore more reliable exposure data are required to perform a more refined exposure estimation and to judge whether a re-evaluation according to the Procedure is needed. Thus for four substances 02.245, 02.250 and 13.076], the Panel agrees with the JECFA conclusion 'no safety concern at estimated level of intake as flavouring substances' based on the MSDI approach.For the six substances, use levels have been provided by the Industry 02.245, 02.250, 02.251, 13.076 and 13.087]. The modified theoretical added maximum daily intake (mTAMDI) figures calculated for three of the six substances 13.076 and 13.087] are above the threshold of concern for their structural classes. For these substances, more reliable exposure data are needed. On the basis of such data, the substances should be reconsidered using the Procedure.

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Section: 2mentioning

confidence: 99%

Scientific Opinion on Flavouring Group Evaluation 90, Revision 1 (FGE.90Rev1): consideration of six substances evaluated by JECFA (68th meeting) structurally related to aliphatic, alicyclic and aromatic saturated and unsaturated tertiary alcohols, aromatic tertiary alcohols and their esters evaluated by EFSA in FGE.18Rev1 and FGE.75Rev1

Flavourings¹

2016

EFS2

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High affinity interaction of Solanum tuberosum and Brassica juncea residue smoke water compounds with proteins involved in coronavirus infection

Dave

Rakholiya

Kaneria

et al. 2020

Phytotherapy Research

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The world is in an immediate need of treatment for coronavirus disease (COVID‐19). Chronic exposure of hydroxychloroquine in the treatment of COVID‐19 may have multiple adverse effects on human physiology, such as cardiac arrhythmias. Natural compounds need to be evaluated as treatment and preventive agents in coronavirus infection. A total of 30 compounds of Solanum tuberosum and Brassica juncea residue smoke water were selected for the virtual screening against SARS‐CoV‐1, SARS‐CoV‐2 and cellular proteins involved in the mechanism of infection. Docking analysis identified lead molecules with favorable binding energy, number of poses and hydrogen bond interactions, which indicates the effective modulation of ACE2 and TMPRSS2 receptors. Results indicated (a) curcumenol, (b) N ‐desmethylselegiline, (c) phentermine and (d) sphingolipid derivatives as a selective and potent candidates in comparison to hydroxychloroquine for COVID‐19 treatment. Our in silico findings, therefore, warrant further in vitro validations of the selected compounds for the discovery of novel preventive and therapeutic drug against SARS‐CoV‐2 infection.

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Machine Learning Techniques applied in risk assessment related to food safety

Crescio

Ingravalle

et al. 2017

EFS3

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In 2014 European Food Safety Authority (EFSA) commissioned this evaluation of the potential use of Machine Learning Techniques (MLTs) to provide insights for the elaboration of a guidance document and to facilitate the harmonisation in EFSA's assessments. Four objectives were provided: 1. To produce an inventory of MLTs that could be of use in the EFSA risk assessment activities; 2. To carry out a classification of EFSA opinions to identify the questions most commonly asked; 3. To assess the performance of ML techniques compared to non-MLTs and to propose a decision tree to help in the choice of the most appropriate methodology; 4. To develop, if possible, machine learning algorithms tailored to answer EFSA specific questions. The extensive literature search on 22 online databases led to an inventory of more than 2.6 million MLTs references: 213,070 abstracts were classified as relevant for EFSA and labelled by applying a Support Vector Machine and a Name co-Occurrences analysis. The application of Latent Dirichlet Allocation and Correlated Topic Modeling to the text of 3,744 EFSA scientific documents allowed the description of 28 main topics characterising the overall activity of assessment carried out by EFSA. Moreover the most common statistical techniques applied in EFSA to address the topics have been identified by text mining and by a questionnaire survey that involved 49 EFSA staff. Six different examples were used to show and compare the different performances of MLTs and non-MLTs techniques: this activity served to develop a decision tree that on the basis of a set of predefined criteria provides a guideline for the selection of fit for purpose MLT. Finally to better address some specific issues, data from the European Union Summary Reports on Zoonoses and on Antimicrobial Resistance were used to develop case studies where existing MLTs were expressly modified. Disclaimer: The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. SummaryMLT deals with the study, the design and the development of algorithms that give computers the ability to learn from experience without being explicitly programmed. In the last two decades, Machine Learning (ML) has become increasingly important with the aim of automatically learning from data, particularly in situations where large collections of data or large, multi-dimensional and heterogeneous datasets are available and/or there is not a recommended mathematica...

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Scientific Opinion on Flavouring Group Evaluation 75, Revision 1 (FGE.75Rev1): Consideration of tetrahydrofuran derivatives evaluated by JECFA (63rd meeting) structurally related to tetrahydrofuran derivatives evaluated by EFSA in FGE.33 (2008)

Cited by 3 publications

References 10 publications

High affinity interaction of Solanum tuberosum and Brassica juncea residue smoke water compounds with proteins involved in coronavirus infection

Machine Learning Techniques applied in risk assessment related to food safety

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