dataesr/scientific-paragraphs-categorization huggingface dataset:
Extreme/super toxicities (class 1) are documented for compound 1 for all routes of administration, and for compound 3 in the case of intravenous administration, where the value of 0. 00 indicates that the com-  In silico computation is, nowadays, an important part of the drug design develop ment process. Using the Molinspiration Cheminformatics software (Molinspiration, a cessed on 27 January 2023, Slovensky Grob, Slovak Republic, 2023, http://www. molinsp ration. com), through an easily obtained SMILES notation for each molecule, we can ca culate certain very useful molecular properties such as the Lipinski's rule of five  [29]. Th compounds 1, 3, 12, 13, 14, 15 and 16 were subjected to this software and we obtained ver promising results of drug-likeness (Table  1 ). The computational prediction of compound toxicities in drug design allows the quic estimation of the lethal doses in animals, and avoids the use of animal experiments.
Th rat acute toxicity of the compounds 1, 3, 12, 13, 14, 15 and 16 was predicted by the in silic software tool GUSAR (General Unrestricted Structure-Activity Relationships, versio 16. 0)  [31]  on the basis of PASS (Prediction of Activity Spectra for Substances) technolog  [33, 34]. The quantitative in silico prediction was applied to four different types of admin istration: oral, intravenous (IV), intraperitoneal (IP), and subcutaneous (SC). As is show in Table  2 , the differences in the predicted LD50 values obtained for the four differen routes, based on the structural formulae of the tested compounds, are reported along wit.
Software The development of the quinidine PBPK model, parameter optimizations, and sensitivity analysis, as well as simulation of different DD(G)I scenarios were performed with PK-Sim (version 11, Open Systems Pharmacology Suite, www. open-syste ms-pharm acolo gy. org). Published plasma concentration-time profiles were digitized with Engauge Digitizer 10. 12 (M. Mitchell, https://marku mmitc hell. github. io/engau ge-digit izer). Model evaluations (i. e. , graph generation and calculation of pharmacokinetic parameters as well as statistics) were accomplished using the R programming language version 4. 2. 1 (The R Foundation for Statistical Computing, Vienna, Austria) and Rstudio 2022. 07. 0 (Rstudio).
Quinidine PBPK model building PBPK model building was initialized by collecting physicochemical and ADME-related parameters of quinidine and 3-hydroxyquinidine from the literature. Additionally, studies reporting quinidine and 3-hydroxyquinidine plasma concentrations alongside subject information and administration protocols were collected. Studies were preferably included if performed in healthy volunteers and if concentration-time profiles were reported alongside unambiguous dosing and regimen information. Gathered concentration-time profiles were split into a model training (model development) and a test dataset (model evaluation). The model training dataset was assembled to (i) maximize the cardinality of the test dataset and to cover (ii) intravenous and oral administration, (iii) the whole dosing range of published studies, and (iv) single and multiple dose administration while preferring information-dense as well as (v) additional measurements of 3-hydroxyquinidine profiles.
Virtual individuals ("mean individuals") were created based on the mean and mode of the reported study demographics if available. By selecting ethnicities according to the study cohorts from the PK-Sim database, varying organ volumes and perfusion rates were taken into account. Relevant enzymes and transporters were implemented according to literature reports and the PK-Sim expression database (see Tables  S1-S3 ). Parameter optimizations were performed to identify suitable quantitative structure-activity relationship methods to calculate cellular permeabilities and partition coefficients. Furthermore, model parameter values that could not be informed from literature reports (e. g. , quinidine intestinal permeability as well as relevant catalytic and transport rate constants) were optimized by fitting model simulations against all studies of the training dataset applying Monte Carlo optimization minimizing the leastsquares objective function.
Quinidine PBPK model evaluation Model performance was evaluated graphically by comparison of population simulation predictions and observed quinidine and 3-hydroxyquinidine plasma concentrationtime profiles. For this, virtual populations of 1000 individuals were generated, based on the study demographics listed in the respective publications, such as ethnic background as well as age and weight range. Additional variability regarding the expression of metabolizing enzymes and transporters was implemented according to the PK-Sim ontogeny database (see Table  S1 ). Furthermore, predicted plasma concentrations for mean individuals, area under the plasma concentrationtime curve calculated between the first and last concentration measurement (AUC last ) and maximum plasma concentration (C max ) values were compared to their respective observed values in goodness-of-fit plots by assessing the proportion of predictions within two-fold of observed concentration, AUC last and C max data.
Quinidine PBPK model A comprehensive quinidine-3-hydroxyquinidine parentmetabolite whole-body PBPK model was built and evaluated using data from 22 clinical studies reporting a total of 43 plasma concentration-time profiles for quinidine. Additionally, two profiles of unbound quinidine and eight plasma concentration-time profiles of 3-hydroxyquinidine were included in the model datasets. In these studies, plasma concentration-time profiles were reported after single intravenous administration of 260. 3-520. 6 mg quinidine gluconate (corresponding to 162. 2-324. 4 mg quinidine base) and single or multiple oral administrations of 0. 1-600 mg quinidine sulfate (corresponding to 0. 08-497. 2 mg quinidine base). The training dataset included 10 profiles of quinidine in plasma, two profiles of quinidine in urine, and five profiles of 3-hydroxyquinidine in plasma.
All relevant quinidine and 3-hydroxyquinidine drugdependent parameters are listed in Table  S5 , information about the expression and localization of relevant proteins is provided in Tables  S1-S3. A selection of population predictions of quinidine and 3-hydroxyquinidine compared to their respective observed data after intravenous and oral administration is shown in Figure  1a-f. Semilogarithmic and linear plots of all modeled studies are shown in Figures  S1-S14. The good descriptive and predictive model performance is displayed in goodness-of-fit plots (Figures  1g-i (h) (h) (h) (h) (h) ( ( ) ) ( ) ( ) ) ) ( ( ) ) ) ( ( ( ) ) ) ( (h) three of three (test dataset) C max GMFEs values were within the two-fold threshold. For quinidine, all predicted V d and half-life values are within twofold of observed values.
To inform the relative contributions of CYP3A4 and P-gp to quinidine metabolism and transport during quinidine model building, data from the carbamazepine-quinidine DDI (i. e. , the extent of 3-hydroxyquinidine formed) was included in the training dataset. Data from the remaining DDIs were used for the evaluation of model predictive performance. Moreover, eight studies were utilized to model DD(G) I scenarios where quinidine and 3-hydroxyquinidine act as inhibitors of CYP2D6 and P-gp. One study was available to assess the effect of CYP2D6 inhibition via quinidine and 3-hydroxyquinidine for the quinidinemetoprolol interaction and one study on the effect of the quinidine-paroxetine-dextromethorphan interactions. Additionally, several DDGI studies in subpopulations with different CYP2D6 activities were available for the victim drugs dextromethorphan (two studies), metoprolol (one study), and mexiletine (one study).
Finally, two studies reported data on the quinidine-digoxin DDI (P-gp inhibition). Multiple studies included plasma concentration-time profiles of multiple compounds, including parent victim drugs, respective enantiomers, and metabolites. For competitive inhibition of CYP2D6, K i values of 0. 017 μmol/L (quinidine  30  ) and 2. 30 μmol/L (3-hydroxyquinidine 10 ) were incorporated from the literature as well as a K i value of 0. 10 μmol/L to describe competitive inhibition of P-gp by quinidine. 31 Information about all utilized studies covering perpetrator and victim drug regimens and subject demographics are provided in Tables  S13  and S14. Population predictions of victim plasma concentrationtime profiles alone or with perpetrator co-administration compared to observed data demonstrated a good DD(G)I model performance (Figures  3  and 4 ).
Metabolism via CYP3A4 shows the largest contribution in vitro compared to other CYP enzymes. 7 However, in vivo metabolism via CYP3A4 was not easily assessable from literature reports, because competitive inhibitors of CYP3A4 in clinical DDI studies showed only a small effect on quinidine plasma concentrations. 26, 33 Therefore, plasma concentrationtime profiles of quinidine and 3-hydroxyquinidine during interaction with carbamazepine, a CYP3A4 inducer, were consulted to serve as a surrogate for lacking in vitro and in vivo data to estimate the relative contribution of CYP3A4 to quinidine metabolism. This approach has been successfully applied before, to estimate the previously unknown contribution of CYP3A4 and tubular secretion (also mediated via P-gp) in a PBPK model of trimethoprim. 17 Here, a DDI study with rifampicin, a CYP3A4 and P-gp competitive inhibitor and inducer, was included in the training dataset during the model building process, leading to a favorable description of trimethoprim concentrations in plasma and fractions excreted in urine.
, S15, S16), where 94%, 100%, and 100% of quinidine training dataset, 90%, 97%, and 91% of quinidine test dataset, 79%, 100%, and 80% of 3-hydroxyquinidine training dataset and 89%, 100%, and 100% of 3-hydroxyquinidine test dataset predicted plasma concentrations, AUC last and C max values were within twofold of the corresponding observed values, respectively. Moreover, nine of 10 quinidine training dataset, 30 of 33 quinidine test dataset, five of five 3-hydroxyquinidine training dataset, and three of three 3-hydroxyquinidine test dataset predicted plasma concentration profile MRDs were less than two. For quinidine, 10 of 10 (training dataset) and 32 of 33 (test dataset) AUC last GMFEs, and eight of eight (training dataset) and 30 of 33 (test dataset) C max GMFEs were below two. For 3-hydroxyquinidine, five of five (training dataset) and three of three (test dataset) AUC last GMFEs, and four of five (training dataset) and F I G U R E 1 Quinidine physiologically-based pharmacokinetic modeling performance evaluation.
(a-f) Predicted compared to observed plasma concentration-time profiles of quinidine and 3-hydroxyquinidine after (a) intravenous and (b-f) oral administration. Population geometric means are shown as lines, geometric standard deviations are shown as shaded areas, and observed data are shown as dots (training dataset) and triangles (test dataset) (±standard deviation, if reported). 8, [25] [26] [27] [28] [29] (g-i) Goodness-of-fit plots comparing predicted and observed (g) plasma concentrations, (h) area under the plasma concentration-time curve calculated between first and last concentration measurement (AUC last ) and (i) maximum plasma concentration (C max ) values. The solid line represents the line of identity, whereas 1. 25fold and two-fold prediction limits are shown as dotted and dashed lines, respectively.
CONFLICT OF INTEREST STATEMENT D. T. is an employee of Sanofi. D. T. uses Open Systems Pharmacology software, tools, or models in his professional role. D. T. and T. L. are members of the Open Systems Pharmacology Management Team. S. F. uses Open Systems Pharmacology software, tools, or models in his professional role. S. F. is a member of the Open Systems Pharmacology Sounding Board. All other authors declared no competing interest for this work.
• Metal-organic frameworks (MOFs) are promising porous nanocarriers which have generated growing interest over the past twenty years. The originality of these structures resides in the combination of metal ions and organic ligands, which assemble to form highly porous networks. This feature enables specific surfaces ranging up to almost 2000 m 2 /g for biocompatible formulations, favorable for drug entrapment  [73, 74]. The diversity of MOFs is such that nearly one hundred thousand different models are currently deposited in the Cambridge Structural Database. Nanosized MOFs are studied for biomedical applications (infectious diseases and cancers), as well as for industrial uses (gas storage and separation, catalysis, water treatment).
Conclusions and Perspectives The objective here was to give an overview of how nanotechnologies could improve the treatment of TB. Clearly, this topic attracts growing interest, as evidenced by the hundreds of publications dedicated to the subject. In contrast, the transition of the NPs to the clinical stage is not achieved yet. In the introduction, a relatively modest amount (about fifty) of nanoformulations currently involved in clinical trials was mentioned, which are for all diseases, but there were no candidates yet for TB  [168]. Of note, several nanoformulations are currently going through clinical trials for the treatment of COVID-19 (clinicaltrials. gov, accessed on January 11th, 2023). Current research for the treatment of TB seems to be more focused on the discovery of new active molecules  [169]  and new vaccines (some of which, nevertheless, contain NPs)  [170, 171].
Conclusions and Perspectives The objective here was to give an overview of how nanotechnologies could improve the treatment of TB. Clearly, this topic attracts growing interest, as evidenced by the hundreds of publications dedicated to the subject. In contrast, the transition of the NPs to the clinical stage is not achieved yet. In the introduction, a relatively modest amount (about fifty) of nanoformulations currently involved in clinical trials was mentioned, which are for all diseases, but there were no candidates yet for TB  [168]. Of note, several nanoformulations are currently going through clinical trials for the treatment of COVID-19 (clinicaltrials. gov, accessed on 11 January 2023). Current research for the treatment of TB seems to be more focused on the discovery of new active molecules  [169]  and new vaccines (some of which, nevertheless, contain NPs)  [170, 171].
Bioinformatics analysis All raw sequences from this study are available in the National Center for Biotechnology Information (NCBI) sequence read archive under the accession number PRJNA937442. Raw sequences were processed with QIIME2 (2022. 2)  [45]. Sequences were demultiplexed using the q2-demux plugin. Quality was assessed using the quality score as per sequencing base graphs; appropriate trimming and truncation parameters were determined based on a quality score of 20. Raw sequences were filtered for quality, denoised, merged and chimeras were removed with DADA2 pipeline via the q2-dada2 plugin  [46]. In details, 15 bp were trimmed off of the front of each reads; forward reads were truncated after 271 bp and reverse reads were truncated after 190 bp, in order to remove low-quality portion of the sequences.
This resulted in a total of 287,665 sequences obtained from 54 samples with 476 ASVs. Rarefaction was used to normalize the data based on sequencing depth in all samples. Only samples with more than 824 sequencing depth were retained for analysis, chosen depending on the minimum number of samples obtained (minimum 6 samples for each dietary treatment). The generated dataset comprised 18,34 % of the reads generated and included 75,93 % of the samples. After filtering and rarefaction analysis, 184,234 sequences remained with a total of 41 samples and 429 ASVs (6 samples retained for GLE and TVE, 7 for E and GL and 8 for TV). By reaching a plateau, rarefaction curves (Supplementary data, Fig. S1 ) indicated that sufficient sequencing depth was achieved, which is also supported by the Good's coverage index around 99.
85 ± 0. 004 %, indicating that most of the species within the samples were obtained  [47]. The obtained amplicon sequence variants (ASVs) were then assigned to taxonomy, using the q2-feature-classifier plugin  [48]  (classify-sklearn method) with a trained Naïve Bayes classifier (using our respective primer sequences) on the SILVA 138 database, at 99 % similarity  [49, 50]. All sequences assigned for Eukaryote/Chloroplast and singletons were removed. Phylogenetics and non-phylogenetics diversity metrics were then determined with the q2-diversity plugin. For phylogenetic diversity analysis, Mafft via the q2-alignment plugin and Fasttree via the q2-phylogeny plugin was first used to align the sequences and generate a phylogeny  [51, 52]. Differences in α-diversity within dietary treatments were evaluated with 4 indices: Pielou's evenness  [53] , Shannon's index  [54] , Simpson's index  [55]  and Faith's phylogenetic diversity  [56].
2 (PICRUSt2)  [63]  with the QIIME2 q2-picrust2 plugin (v2. 5. 0). Briefly, the ASVs representative sequences were aligned with HMMER  [64]  and then inserted into a reference tree based on 20,000 16S sequences from genomes in the Integrated Microbial Genomes database using EPA-NG  [65]  and gappa  [66]. Hidden state of genomic content was then predicted after normalizing ASVs by 16S copy number using castor  [67]. Finally, biological pathway abundance were then inferred using MinPath  [68] , on the MetaCyc database based on Enzyme   [69, 70]. All tests were carried out within QIIME2; RStudio was used to export and graphically display the diversity measures with ape  [62]  and ggplot2  [41]  packages. The differences were considered statistically significant at p-value < 0. 05.
Calculation and statistical analysis Fish growth parameters were calculated over the 7-weeks period as follow: Relative weight gained (%) = 100 × (W final -W initial )/W initial ; and Feed Conversion Ratio (FCR) = 100 × (amount fed/W gained ); with W for weight. Differences in immune parameters and relative gene expression between treatments were determined using a one-way analysis of variance (ANOVA), after that normality and homoscedasticity of the distribution hypothesis were verified, followed by a Newman-Keuls post-hoc test. Post hoc tests were performed using the agricolae package ver. 1. 3. 5  [40]. The differences were considered statistically significant at p-value < 0. 05. Statistical analyses were performed using RStudio ver. 4. 2. 0 software  [39]. Graphics were drawn using the ggplot2 packages ver.
Acknowledgements We wish to earnestly thank  Jean-Marc Coupé  for his technical assistance and maintenance of fish all along the project. Thanks to  Tina Terrene ,  Marjorie Vignaud , and  Erwan Caudal  for their help and expertise in fish handling and sampling. We also thank  Frédéric Terrier  for his help with the feed formulation and preparation. We are grateful to  Pauline Pannetier  and  Alexandra Rahmani  for technical assistance with flow cytometry and helpful discussions. We sincerely would like to thank all members of  Anses-Plouzané and INRAe-PEIMA  for their collective help and support, and for providing essential facilities needed for this study.
| Definitions • Valid SAEs: to be considered valid, registered, and coded in the Inserm safety database, an SAE had to have occurred between D0 (inclusion date) and D29 (last protocol visit) and contain the minimum following information: patient ID, AE description, IMP, and investigator name. • Reference safety information (RSI): the expectedness of Serious Adverse Reactions (SARs) was defined in accordance with the RSI section contained in the reference document of each IMP.
| Serious adverse event (SAE) reporting According to the EU regulation, investigators must report all SAEs immediately and no later than 24 h after becoming aware (see Figure  2 ). Upon receipt of an SAE Form, the Inserm PV Department immediately forwarded the notification to the independent medical officer and when applicable to the industrial partner concerned. The Inserm PV Department was responsible for data entry into the Inserm safety database and for medical evaluation (MedDRA coding, sponsor causality assessment, and expectedness). The Inserm drug safety officer implemented the medical feedback of the independent safety officer. If the SAE was possibly related to the IMP and unexpected according to the RSI section, the SAE was considered as a Suspected Unexpected Serious Adverse Reaction (SUSAR) and had to be promptly reported to competent authorities within the regulatory timeframes.
| Impossibility of real-time and onsite monitoring In clinical studies, pharmacovigilance is directly impacted by onsite monitoring. During lockdown, this monitoring was very difficult to implement due to restrictions on access to hospitals (notably due to a"White Plan" in France). At the beginning of the trial, the sponsor tried alternatives, including remote monitoring, but this needed time to be able to be implemented. The first remote monitoring took place on April 24, 2020, followed by 54 other monitoring visits set up in 11 different centers during the French lockdown. The COVID-19 data generated from the DisCoVeRy trial were regularly analyzed for the evaluation of the safety and efficacy of repurposed drugs. The lack of implementation of real-time monitoring may have led to under-reporting of SAEs, although SAE reporting was in the end very acceptable.
| DSMB meeting During the first months of the trial, the DSMB met on 21 March, 20 April, 27April, and 11 May. The DSMB members were informed that all data transmitted to them were not monitored. To perform better statistical analysis, all safety data were extracted from the Inserm safety database. This decision had important consequences, including the importance of a real-time quality control of all SAEs and the harmonization of their coding. Regarding.
Several online documentation and database focused on ethnobotany, chemistry, biodiversity, dyes and pigments or medicinal plants have been as well consulted. Furthermore, all the information obtained from the reviewed literature has been combined and supplemented by information obtained from the ethnobotanical survey of Reunion Island's dye plants conducted by a team leaded by Christophe Lavergne from CBNM. This survey from January 2021 to November 2022 relied on the local peoples and stakeholders who have a good knowledge of the history and culture of Reunion Island including local dyers, farmers, small cosmetics manufacturers, botanists, etc. Ethnobotanical data were recorded via face-to-face interviews in the local language (Créole) or French, depending on the interviewee. The questionnaire concerned the knowledge of the traditional uses of some plant species as coloring materials, and the survey enabled us to inventory additional plant species traditionally uses in Reunion Island for natural dyeing.
5) during 48 h in order to generate crude dry color extract in powder (i. e. , the plant-based dyestuff ) for pigment extraction yield calculation. All extracts were kept at 4 °C until further use. To check the coloring property of the dry color extracts, the sample was dissolved on the same solvent used for the extraction at different concentrations, i. e. , from 1. 0 to 32 mg/mL of dry powder extract in solution, to show the evolution of the shade of the sample by their color value measured in the international CIELab color system in terms of L* (lightness), a* (greenness versus redness), and b* (blueness versus yellowness). The color coordinates were analyzed using a spectrophotometer (Konica Minolta CM-3600A) with 10 mm pathlength quartz cuvette, combined to the color data software SpectraMagic NX (Konica Minolta) for data analysis.