dataesr/scientific-paragraphs-categorization huggingface dataset:
This could be e. g. intentional ingredients, impurities thereof, contaminants or packaging material migrants. Whether an exposure to a compound is intentional or unintentional does not matter for safety assessment. Toxicology data for those cosmetics-relevant structures were then mined, curated in a database, submitted to quality control and relevance assessment, and PODs selected for threshold derivation. The data searches for the COSMOS project primarily aimed at using well established databases and data sources with primarily pre-reviewed toxicity data, for example the US EPA ToxRef DB (US Environmental Protection Agency Toxicity Reference Database) and IRIS (Integrated Risk Information System), SCCS Opinions, US FDA PAFA/CFSAN (Priority-based Assessment of Food Additives/Center for Food Safety and Applied Nutrition).
Still, it was confirmed that aspects of quality control and comparability with prior existing TTC datasets need to be taken into consideration. Twentyfive percent of the PODs of the COSMOS TTC dataset comprising 552 chemicals was reviewed manually for study relevance and reliability during the step of choosing one POD per chemical. Comparison of different POD distributions demonstrated that data evaluation for the 10th percentiles and Cramer Class assignments have more impact on the thresholds than choice of e. g. time extrapolation factors or exclusion of certain structural classes. The thresholds derived for the COSMOS TTC dataset and a combined COSMOS / Munro dataset were similar to multiple other TTC non-cancer datasets, demonstrating robustness of the approach. For comparison, Table  1  lists the 5th percentiles of POD distributions for different published TTC non-cancer datasets.
These PODs are either chronic study No Observable Adverse Effect Levels (NOAELs) or the duration-corrected NOAELs of subchronic or subacute studies and would be divided by an additional assessment factor of 100 to derive the respective TTC thresholds. Chemical space comparison of the new COSMOS TTC dataset versus the incumbent Munro dataset  (Munro, 1996)  demonstrated that there was significant overlap between the datasets, but that also new chemical classes had been added to the TTC dataset, specifically cationic surfactants, hair dye components and organo-silicones. Not surprisingly, the dataset with predominantly cosmetics-related compounds contained less potent substances and expressed higher TTC thresholds for Cramer Classes I and III. The combination of the existing Munro dataset with the COSMOS TTC dataset ('federated') resulted in a TTC dataset with a significantly higher number of structures (> 900), again increasing the probability that any new to-be-assessed structure will fall within the chemical and biological space of predictivity of the TTC thresholds.
Still, it needs to be noted that several types of compounds are not amenable to assessment by TTC, as they were not included in the datasets (metals, proteins, radioactive materials) or were excluded due to extreme potency (aflatoxins, N-nitroso-and acetoxycompounds, polyhalogenated dioxins/furans, steroids). Also, TTC thresholds refer to oral systemic toxicity, not local effects at portal of entry. For non-volatile compounds, even if dermal exposures prevail, in vivo toxicity testing is typically performed by the oral route, as this route of administration results in higher systemic exposures in the majority of cases, and "in practice, oral route studies are often used for the MoS calculation to consider systemic exposure" (SCCS (Scientific Committee on Consumer Safety), 2018). The application of toxicity data obtained by oral administration to assess dermal exposure via personal care products/cosmetic products is the default case in personal care risk assessment and requires considerations on oral and dermal systemic bioavailability.
With that, TTC is not a layman tool, but requires broad knowledge in different aspects of hazard and risk characterisation and familiarity with the concept itself. To scientists, not involved in the area, it might seem as if there were an inconsistency between the outcomes of single projects and respective published thresholds during the last decade. However, the publication of multiple slightly different thresholds is rather an expression of progress in expanding datasets and understanding the influence of different study types, chemistries and assessment approaches. Moreover, the fact that the different thresholds were relatively comparable demonstrates that the approach is reproducible and robust. As a general rule, TTC {and (Q)SAR} risk assessment should be based on the data which maximally combine the aspects of being up-todate, data reliability, large database and large chemical space.
Out of the datasets listed in Table  1 , the federated data set of Yang et al. (Yang et al. , 2017)  currently has the best overlap of those criteria. The resulting TTC are for Cramer Class I (CCI), 2700 μg/person/day or 46 μg/ kg bw/day, respectively; for CCII 370 μg/person/day or 6. 2 μg/kg bw/ day and for CCIII 140 μg/person/day or 2. 3 μg/kg bw/day (body weight default value for adults is 60 kg). The reproductive-developmental datasets of Table 1  (Laufersweiler et al. , 2012 and van Ravenzwaay et al. , 2017)  provide assurance that those specific endpoints are adequately covered when applying thresholds based on general toxicity. As with other frameworks, the TTC concept should be constantly improved as far as possible. Regulatory applications will benefit from regular reviews by international expert groups of the progress made, to achieve consensus on if and how the established approach described by the EFSA/WHO report (EFSA (European Food Safety Authority), World Health Organization (WHO) 2016) should be adapted to progress.
Partosch et al. (Partosch et al. , 2015)  attempted to derive an iTTC by adjusting the external NOAELs of substances from three databases by in silico estimates of bioavailability. The oral bioavailability prediction method used by Partosch et al. (Partosch et al. , 2015)  takes into account passive oral absorption but does not consider the possible impact of active transport (either influx or efflux), pre-systemic metabolism, systemic metabolism and clearance or any of the important factors that would impact internal exposure levels to target tissues after passive uptake from the gastro-intestinal (GI) tract. Animal versus-human differences in metabolism or other kinetic determinants for the chemicals were also not taken into account. The estimates provided were still based on external dose and not an internal exposure metric, such as plasma concentration.
, 2019). The remainder of this section will therefore highlight some of the key outcomes from this ongoing work. The proposed approach from Ellison et al. (Ellison et al. , 2019)  for deriving iTTCs is shown in Fig. 3. The approach starts with identifying an existing TTC dataset which contains chemical specific NOAELs expressed as an external dose in mg/kg/day. For each chemical within the dataset, a literature search is completed to identify existing metabolism and Pharmacokinetic (PK) data and in silico estimates of ADME parameters are predicted. For selected chemicals, new in vitro ADME data (e. g. metabolism, permeability, binding) may be generated. Chemical-specific PBPK modelling is then conducted to convert the chemical-specific NOAEL from the toxicity study to an internal blood concentration Css, AUC, or Cmax for each chemical.
The PBPK modelling uses the appropriate species, dose, and route from the toxicity study. The distribution of chemical-specific Css values will then be evaluated and an appropriately low (e. g. 5th percentile) Css threshold will be identified for the group of chemicals and by Cramer Class. Uncertainty factors will also be applied to the Css threshold values to derive the iTTC values that can be applied to a human safety assessment. However, additional discussion will be needed to determine which values would be appropriate, given that a comparison of an animal-derived iTTC value to a modelled human blood level for a specific exposure may help quantify extrapolation factors or uncertainty factors to account for interspecies toxicokinetics differences The chemical databases being utilised in the iTTC project are the Munro et al.
(Munro et al. , 1996)  non-cancer TTC database, the COSMOS non-cancer TTC database  (Yang et al. , 2017)  and the Research Institute of Fragrance Materials (RIFM) database. The Munro et al. noncancer TTC database  (Munro et al. , 1996)  is one of the more widely known and cited TTC databases and consists of 613 chemicals, representing a range of industrial chemicals, pharmaceuticals, food substances and environmental, agricultural and consumer chemicals likely to be encountered in commerce. The database contains data from multiple species (rat, mouse, rabbit and hamster) and all data are derived from oral exposure studies. The COSMOS database is a non-cancer TTC dataset enhanced with cosmetic and packaging materials. The toxicity studies used from the RIFM database represent flavour and fragrance materials and contain a large number of chemicals in Cramer Class II, which is a Cramer Class that has been historically underrepresented in TTC databases.
As described in Ellison et al. (Ellison et al. , 2019) , an automated workflow was developed and utilised to locate relevant plasma concentration versus time data and metabolism data for the 1251 chemicals in the combined database of Munro et al. (Munro et al. , 1996) , COSMOS and the RIFM database). The search was performed with respect to a specific species for which the NOAEL data were collected. Manual review of the literature and extraction of the relevant data were then completed. At the conclusion of the literature search, 80 % of the chemicals had no PK or metabolism data, 10 % had PK data, 5% had in vitro metabolism data and 5% had both PK and in vitro metabolism data. The results from the literature search will be used as input into the PBPK models and help identify which chemicals need further in vitro metabolism testing.
Approximately 300 chemicals are currently estimated to be included in the final iTTC chemical database. For the chemicals included in the database, approximately 45 % will have existing PK data in the same species as the toxicity study and all will have in vitro metabolism. The chemicals will span across the existing TTC chemical space, will cover all three Cramer Classes, represent a broad physicochemical space and have NOAELs that span several orders of magnitude. Ongoing work is occurring in several areas of the project. In vitro Caco-2 data are being collected for the iTTC chemicals and will be used as input into the PBPK modelling simulations. In partnership with RIFM and the Long-Range Research Initiative of the American Chemistry Council, cryopreserved hepatocytes (primarily rat, but also mouse, rabbit and dog) are being used to determine the in vitro metabolism rate for iTTC chemicals which do not have existing data in the literature.
Evaluation of the PBPK modelling workflow is being conducted by comparing plasma versus time concentration profiles from the PBPK model to existing PK data from preclinical studies. Also, the PBPK models are being used to estimate the internal exposure associated with the NOAELs from the toxicity studies. Upon successful completion of the project, a set of robust iTTCs will be available for use in a human safety assessment. The availability of iTTCs will expand the possible use of the TTC concept for the human safety assessment. Furthermore, due to the type of data that is needed to do this work, an added benefit of the project is that it will: provide compiled databases of existing PK and metabolism data; generate new in vitro Caco-2 permeability and hepatocyte metabolism data for hundreds of compounds under standardised conditions; develop PBPK modelling workflows for bottom-up In Vitro to In Vivo Extrapolation (IVIVE); characterise the magnitude of uncertainty when doing bottomup PBPK modelling for a broad set of chemicals.
3. Approach for deriving internal Threshold of Toxicological Concern (iTTC) values  (Ellison et al. , 2019)  • ADME • Existing "traditional" toxicity data and PODs As increasing amounts of evidence are compiled there should be a commensurate decrease in uncertainty and hence increase in confidence in the read-across argument. This requires the user to decide when the data and knowledge are sufficient to justify the read-across hypothesis. In silico toxicology can call upon a range of resources to support information gathering. These range from the use of databases of existing toxicity data and data for NAMs,  (Pawar et al. , 2019) , the calculation of physicochemical properties and descriptors for the molecules, the use of in silico toxicity prediction and screening softwares, as well as the assessment of ingredient use and overall exposure.
vegahub. eu) platform with tools integrating in silico models and read-across. Furthermore, the integration among multiple tools is now pointing towards the development of frameworks wrapping models for exposure and hazard within the same system. This is the target of the EU project VERMEER (www. life-vermeer. eu). Within this project, a single software system has been designed, which predicts the skin permeation using in silico models, and then predicts whether the substance is a CMR (Carcinogen, Mutagen, Reproductive toxic), and skin sensitiser. The Cramer classes and NOAEL and Lowest Observed Adverse Effect Level (LOAEL) values are also predicted. Based on the ingredient concentration (provided by the user), and on the results of these exposure and hazard models, the safety of the ingredients at a certain concentration is assessed.
This software will be freely available within VEGAHUB (where anyhow several separate models to be wrapped are already available). It is noticed that the information on the permeation (which can be skipped by the user) moves towards a risk assessment perspective related to the internal dose. This shift requires more studies. Along this direction, a recent project, funded by EFSA, called Optitox, is developing toxicokinetic models to be combined with the hazard in silico models. The expectation is to have more accurate predicted values. An interesting advantage of the in silico models is the possibility to run them on large collections of substances. In a recent study, about 20,000 cosmetic ingredients of botanical origin have been screened for mutagenicity; multiple in silico models have been used, and their results integrated, providing an overall value with the associated uncertainty, related to the level of agreement among the models  (Raitano et al.
, 2019). A higher level of integration among different models can be achieved for prioritisation purposes, combining multiple values on multiple endpoints. The JANUS software is an example. This software has been developed within a project funded by the German UBA (German Environmental Agency), and combines 48 different in silico models, to get a single CMR, Persistent, Bioaccumulative, Toxic (PBT) and endocrine disruption assessment. In this case, for carcinogenicity and reproductive toxicity, quantitative assessment is provided, offering the way to sort the substances to be prioritised in a continuous way. For this purpose, the in silico models for carcinogenicity include four classifiers and two regression models, based on the slope factor for inhalation and oral exposure. Further challenges for in silico models are the possibility to plan safer substances, substituting those with higher concern.
Selection of source chemicals by structural similarity The selection of the source chemicals was based on three key criteria. Firstly, structural similarity of more than 50 % was assessed by using the ChemIDplus structural similarity tool (https://chem. nlm. nih. gov/chemidplus/) with the 1,4-benzenediamine (PPD) free base structure in combination with the 2-methyl-side chain (target structure) and the target structure 2-methyl-1,4-benzenediamine (PTD) alone. Secondly, retrieved compounds were manually filtered to match the benzenediamine structure with amino groups in positions 1 and 4 (see Fig. 5 , red circles) and side chains in position 2 (blue circles). This was considered to set appropriate limits for the potential biological reactivity (see Table  2 below ). Thirdly, compounds with relevant data for repeated dose toxicity were selected.
Assessment of read-across plausibility regarding confidence For this specific assessment a high confidence categorization was applied based on the data quality. Three suitable analogues with contributing data linked to highly robust datasets for repeated dose toxicity (90-day studies), a high concordance in the observed toxicity and the resulting NOAELs have been identified (see Table  2 ). This high confidence level is further supported by a similar biological reactivity pattern towards cysteine (including similar depletion rate for target and analogue PPD). The read-across prediction to PTD is therefore considered equivalent to standard testing. Subsequently, PPD is selected here as the analogue with highest biological reactivity and the highest severity of the toxic effect for repeated dose toxicity.
Considering systemic exposure Exposure under application conditions of hair dyeing occurs on the skin (scalp) at low concentrations in the range of 2% (SCCS (Scientific Committee on Consumer Safety), 2010, 2012a; SCCS (Scientific Committee on Consumer Safety), 2012b, 2013), and detoxification by N-acetylation to non-reactive derivatives is the key biotransformation (see Table  2 ). By assessing metabolic transformation rates in vitro in human skin explants (applying typical hair dye use conditions) as well in human keratinocyte cultures (HaCaT cell line) and human hepatocyte cultures, we estimated the remaining systemic exposure (AUC) to the parent compounds PPD (analogue) and PTD (target). For both compounds the estimation revealed similar AUC values. For PPD the estimated AUC data were found to be in the same order of magnitude as those published for human hair dye consumers (for details see  Manwaring et al.
Strategy used for the assessment Read-across was used to address this data gap and to derive a POD that was subsequently used to calculate a Margin Of Internal Exposure (MOIE) building on the systemic exposure predictions and relative potency data obtained using the NAMs. Uncertainties were assessed and an overall conclusion on the use of NAMs to support read-across and perform a human-relevant risk assessment was reached. Supporting information based on the comparison of human exposure estimates to in vitro activity concentrations was also explored. The formation of a MoA hypothesis was central to this case study. The available in vivo data on the source substances (methyl, ethyl and butyl paraben) as well as physicochemical properties, in silico predictions and in vitro data on the source and target substances were evaluated to arrive at the hypothesis that short chain parabens with alkyl chain lengths of 1-4 carbons have a similar biological activity (low toxicity with weak estrogenic activity), and that their common major metabolite 4-HBA shows no appreciable estrogenicity.
It was also hypothesised that alkyl chain length differences would result in differences in bioavailability and a predictable potency trend. In silico alerts were also found for Androgen Receptor (AR) and Thyroid Hormone Receptor (THR) binding that were also addressed. In order to test the MoA hypotheses of weak estrogenic activity and the potential for interactions with AR and THR, a suite of robust and human-relevant transcriptional activation assays was performed, which provided dose-response information. Data were generated for both the target and source substances and 4-HBA in the CALUX EATS activity panel (BioDetection Systems, NL) in the presence and absence of S9. This demonstrated that metabolism of the esters to the acid resulted in a loss of the weak endocrine activity. This enabled the derivation of potency scaling factors (Toxcast ER model), which were applied in the safety assessment to a conservative in vivo rat NOAEL for butylparaben of 2 mg/kg/day  (Fisher et al.
Concluding remarks The workshop provided an up to date overview of selected NAMs and strategies for the safety assessment of cosmetic ingredients. Speakers and participants actively and openly shared their (personal) views, which allowed a good scientific exchange. For the SCCS, this workshop was timely since the safety assessment of new cosmetic ingredients will have to rely on new non-animal concepts. These may include NGRA and WoE approaches in which NAMs, concepts such as TTC and/or iTTC and read-across are combined with historical animal data. Animal data that are generated for another regulatory purpose than cosmetics may also be included in the safety assessments, but some companies choose to not use any post-ban animal test data. Progress has clearly been made, but more examples are needed to create confidence that performing NGRA as described above is protective, also for new compounds.
In Silico Search and Analysis of IMEs Encoding a Relaxase or an Integrase Related to the One of IME_SagLMG15084_oriT The relaxase (GenBank ID: EPW57073. 1) and integrase (GenBank ID: EPW57075. 1) of IME_SagLMG15084_oriT described previously  [15]  were used as queries to find homologs by BlastP and tBlastN analyses (with default parameters on non-redundant protein sequences and nucleotide databases, respectively; last NCBI database interrogation on 2020/02/11). Hits with more than 40% of amino acid identity with the whole query sequence were further analyzed. Sequences adjacent to the identified genes were extracted from genomes or contigs and then manually explored using Geneious Prime (version 2020. 0. 5, Biomatters Ltd. , Auckland, New Zealand to identify the boundaries of the elements (search of direct repeats).
Excision Tests and Mating Experiments Excision tests were done as described previously  [21]. Sequence of the primers used is indicated in Table  S1. Relevant amplicons, attI and attB were sequenced (Eurogentec, Angers, France). Mating experiments using S. salivarius and S. thermophilus strains were made as described previously  [21, 24]. All the experiments done are listed in Table  S3. At least three technical replicates of three independent biological replicates were done. Data are expressed as means ± SD. Statistical analysis was performed using the paired Students t-test. A p-value < 0. 05 was considered as significant.
IME_oriTs Identified in S. salivarius Strains L11, L25 and L45 Are Able to Excise from the Chromosome In both L11 and L25 S. salivarius strains, an IME_oriT is integrated into oriT of an ICE of the ICESt3 family, which is integrated in the 3' end of fda. In S. salivarius L45, another IME_oriT is integrated in oriT of an ICESt3-related ICE integrated in the 3' end of rpsI. Excision of these three IMEs and of their host ICEs was tested by PCR for wild type strains. All the six integrative elements (IMEs and ICEs) were found to excise (Figure  2 , data shown for elements of strain L25). S1.
IME_oriTs Identified in S. salivarius Strains L11, L25 and L45 Are Able to Excise from the ChromosomeIn both L11 and L25 S. salivarius strains, an IME_oriT is integrated into oriT of an ICE of the ICESt3 family, which is integrated in the 3 end of fda. In S. salivarius L45, another IME_oriT is integrated in oriT of an ICESt3-related ICE integrated in the 3 end of rpsI. Excision of these three IMEs and of their host ICEs was tested by PCR for wild type strains. All the six integrative elements (IMEs and ICEs) were found to excise (Figure2, data shown for elements of strain L25).