dataesr/scientific-paragraphs-categorization huggingface dataset:
9 and a 50% range narrower than the AUC last ratio (Figure  3C ). PBPK-based predictions using ML-driven parameters (Table  1 ) were also tested against the same PK profile dataset. Figure  4  shows individual profiles along with PBPK-based predictions using in vivo CL for the different distribution models investigated. The PBPK model predictions appear to satisfactorily describe the majority of the compounds' PK profile. The PBPK model was able to capture the bi-exponential nature of the compounds' plasma distribution. Comparing the different distribution models, simulations show that there are cases where all of them conclude in similar exposure prediction (e. g. , compound 5 and others), whereas, for cases such as compound 60 or 61 and others, the PK profile has significantly different characteristics depending on the distribution model used.
Database survey A search conducted in the Cambridge Structural Database (CSD; Version 5. 42, last updated in May 2023;  Groom et al. , 2016)  targeting N-substituted isatin derivatives yielded a total of 58 results. Among these, there were five reports on the structure of isatin itself and four instances of the structure of N-methylisatin. Thirteen of these structures featured an alkyl chain consisting of two or more carbon atoms. The compound most closely related to the title compound is 1-(3-bromopropyl)-1H-indole-2,3-dione (CSD refcode AKOBIN;  Qachchachi et al. , 2016) , which also features a gauche terminal C-C-C-Br grouping.
When the exponential phase was identified between only two days, the formula µ = (LnN i -LnN 0 )/(t it 0 ) was applied, N 1 and N 0 being the cell concentrations at time i (t i ) and 0 (t 0 ), respectively. Cell biovolume was measured in the experimental samples in order to have an additional parameter to compare the Adriatic and NW Mediterranean strains. Cell size parameters were measured from images captured with a camera connected to the Leica DMi1 inverted microscope and processed with the free computer program ImageJ  [81]. For each treatment and selected sampling day (Table  S3 ), the dorsoventral diameter (DV) and width (W) of 100 cells were measured. The anteroposterior diameter (AP) was calculated using a fixed DV/AP factor based on the data presented by  Guerrini et al. (2010) , given that the DV and W of the cells in the present experiments fell in the range of that study  [41].
Funding: This research was conducted with the financial support received by N. I. M. -P. through the fellowship  FISDUR2020-00254  ( AGAUR, Government of Catalonia ), project  PGC2018-095013-B-I00  ( Spanish Ministry of Science, Innovation and Universities) , and project  2017SGR-310  ( Agency for Administration of University and Research Grants, Government of Catalonia, Spain). N. I. M. -P. is also grateful for the Ph. D. research financial assistance of the  Water Research Institute (IdRA) of the University of Barcelona. The study was also supported by the  Ostreorisk  project ( CTM2014-53818-R ,  Ministry of Economy, Industry and Competitiveness)  and  CoCliME  project funded by the  ERA4CS Toxins 2023, 15,  188 21 of 24 Network (ERA-NET)  initiated by  JPI  Climate and co-funded by  EPA (IE) ,  ANR (FR) ,  BMBF (DE) ,  UEFISCDI (RO) ,  RCN (NO) ,  FORMAS (SE) , and the  European Union  (Grant No.
All Clausena extracts were profiled by LC-HRESIMS 2 as previously described  [2] [3] [4]. Briefly, the samples were analyzed using a data-dependent acquisition mode. The resulting spectral data were preprocessed via MZmine 2  [10]  and structured into molecular networks  [11]  using Metgem  [12]  (Figure  1A ). The color code indicates which extract(s) each ion comes from. A cluster quasi-specific to the leaf extract of C. harmandiana (Figure  1B , in red) was highlighted. As the only active extract, the compounds corresponding to these ions were most likely responsible for the antiviral activity. Therefore, they were subjected to targeted isolation. The EtOAc crude extract was first subjected to silica gel flash column chromatography, yielding 11 fractions (F1-F11); subsequently, the fractions were analyzed using LC-HRESIMS.
Cytotoxicity and H-CoV-229E Inhibition Assays Compounds 1-3 were first evaluated for their cytotoxic activities against Huh7, a h man hepatocyte cell line. All compounds showed cytotoxic activities in the micromol range (CC50 between 0. 5 and 1. 3 µM) (Table  2 ). The maximum non-toxic concentration th provides more than 95% viability was 0. 25 µM for all three tested compounds. This co centration was then used as the highest concentration for antiviral assays. Thus, to asse ). NMR data were almost the same to those of 1 but suggested the presence of one additional methylene. Indeed, the 13 C-NMR spectrum (Table  1 ) showed an extra carbon at 32. 3 ppm. Its position was determined thanks to HMBC correlations (Figure  3  The NMR data of compound 3 were almost identical to those of 1 but without a double bond, as deduced from the absence of carbon around 130 ppm in the 13 C-NMR spectrum (Table  1 ).
min -1 , drying gas temperature-240 • C. MS scans were operated in full-scan mode from m/z 100 to 1400 (at 6 Hz). MS 1 scan was followed by MS 2 scans of the five most intense ions above an absolute threshold of 2000 counts. Selected parent ions were fragmented with a collision energy fixed at 30 eV and using an m/z dependent isolation window of 2-4 amu. The mass accuracy was guaranteed via an injection of a calibration solution from sodium formate clusters with external (at the beginning of each run) and internal (segment 0. 1 at 0. 4 min of each sample) calibration by a High Precision Calibration (HPC) equation with a maximum mass delta of 1 ppm and 7 as the minimal number of calibration points. LC-UV and MS data acquisition and processing were performed using DataAnalysis 4. 4 software (Bruker Daltonics, Bremen, Germany).
MZmine 2 Pre-Processing The MS 2 data files were converted from the. d Agilent standard data format to the. mzXML format using MSConvert software (part of the ProteoWizard package (Palo Alto, CA, USA, v3))  [23]. The. d Bruker data files were converted to. mzXML format using DataAnalysis 4. 4 software. All. mzXML were then processed using MZmine 2 v53  [10]. Mass detection was conducted using a noise level of 400 counts for MS and 0 count for MSMS dimension. The ADAP chromatogram builder was used with a minimum group size of scans of 4, a group intensity threshold of 3000, a minimum highest intensity of 4000, and an m/z tolerance of 15 ppm  [24]. The ADAP wavelets deconvolution algorithm was used with the following standard settings: S/N threshold = 8, minimum feature height = 3000, coefficient/area threshold = 10, peak duration range-0.
02-1. 0 min, RT wavelet range-0. 01-0. 07. Isotopologues were grouped using the isotopic peaks grouper algorithm, with an m/z tolerance of 15 ppm and an RT tolerance of 0. 1 min. MS 2 scans were paired using an m/z tolerance range of 0. 025 Da and RT tolerance range of 0. 1 min. Peak alignment was performed using the join aligner module (m/z tolerance = 15 ppm, weight for m/z = 1, weight for RT = 1, absolute RT tolerance = 0. 1 min). The peak list was gap-filled with the peak finder module (m/z tolerance = 5 ppm and RT tolerance = 0. 05 min). Eventually, the. mgf spectral data file and its corresponding. csv metadata file (containing RT and peak areas) were exported using the dedicated "Export to GNPS-FBMN" built-in module  [11, 25].
Cells and Culture Conditions Human-derived hepatoma cells, Huh7 (ATCC, PTA-8561) cells, and Vero-E6 cells were grown in Dulbecco's Modified Eagle Medium (DMEM, PAN Biotech, Aidenbach, of infected cells. The percentage of infected cells was assessed using Cytexpert software (version 9. 00, La Jolla, CA, USA).
Statistical Analysis and IC 50 and CC 50 Determination Both the Cytotoxic concentrations (CC 50 ) and inhibitory concentrations (IC 50 ) were obtained by performing nonlinear regression followed by the construction of sigmoidal concentration-response curves. The results are means ± SD of at least three independent experiments performed in triplicate and are expressed as relative value compared to untreated infected cells (Mock-treated). Statistical analyses were conducted using one-way ANOVA. All statistical analyses were carried out using GraphPad Prism software (version 9. 0; La Jolla, CA, USA). Significance levels are shown in the figures as follows: * p < 0. 05; ** p < 0. 01; *** p < 0. 001, **** p < 0. 0001, n. s. = not significant.
Biological Evaluation All synthesized compounds 1-27 have been submitted to the National Cancer Institute (NCI) (Germantown, USA) and have been selected for evaluation in the NCI-60 cellline screen. Compounds are generally selected for screening based on their ability to add diversity to the NCI small molecule compound collection  [36]. The NCI-60 panel includes cell lines from nine tumor types (leukemia, non-small cell lung cancer, colon cancer, CNS cancer, melanoma, ovarian cancer, renal cancer, prostate cancer and breast cancer) and is extremely well characterized at the molecular level with both in-house and crowd-sourced characterization, including exome sequence for mutations, SNPs, DNA methylation, metabolome, mRNA, microRNA, and protein expression. This molecular characterization dataset enables interrogation of patterns of growth inhibition by the investigational drug set looking for characteristics of the cell lines that determine sensitivity.
Biological Evaluation All synthesized compounds 1-27 have been submitted to the National Cancer Institute (NCI) (Germantown, MD, USA) and have been selected for evaluation in the NCI-60 cell-line screen. Compounds are generally selected for screening based on their ability to add diversity to the NCI small molecule compound collection  [36]. The NCI-60 panel includes cell lines from nine tumor types (leukemia, non-small cell lung cancer, colon cancer, CNS cancer, melanoma, ovarian cancer, renal cancer, prostate cancer and breast cancer) and is extremely well characterized at the molecular level with both in-house and crowd-sourced characterization, including exome sequence for mutations, SNPs, DNA methylation, metabolome, mRNA, microRNA, and protein expression. This molecular characterization dataset enables interrogation of patterns of growth inhibition by the investigational drug set looking for characteristics of the cell lines that determine sensitivity.
Selected compounds 1-27 have been tested initially at a single high dose (10 µM) in the full NCI 60-cell panel. The one-dose data is reported as an average of the percent growth of treated cells. The number reported for the single dose (10 µM) test is the cell growth relative to the no-drug control and relative to the cell count at time zero. This allows the detection of both cell growth inhibition (values between 0 and 100, cytostatic effect) and cell lethality (values less than 0, cytotoxic effect). The same screening methodology is applied for the 5-dose test, also realized by NCI on the most promising compounds identified in the one-dose test. To the best of our knowledge, this is the first study of 7-chloroquinolinehydrazones on such a large number of cancer cell lines. Among tested compounds 1-27 in the one-dose screen, seventeen hydrazones 1, 2, 6, 7, 9, 12-20, 22, 23 and 25 were the most active, while the others did not have any impact on cancer cell growth.
The most effective cancer cell growth inhibition has been achieved with hydrazone 23 (Table  2 ). Therefore, several structure-activity relationships could be determined. Selected compounds 1-27 have been tested initially at a single high dose (10 µM) in the full NCI 60-cell panel. The one-dose data is reported as an average of the percent growth of treated cells. The number reported for the single dose (10 µM) test is the cell growth relative to the no-drug control and relative to the cell count at time zero. This allows the detection of both cell growth inhibition (values between 0 and 100, cytostatic effect) and cell lethality (values less than 0, cytotoxic effect). The same screening methodology is applied for the 5-dose test, also realized by NCI on the most promising compounds identified in the one-dose test.
Acknowledgments: The authors acknowledge the  Executive Unit for Financing Higher Education, Research, Development and Innovation (UEFISCDI) ,  Romania , for financial support of this work which is part of the project  PN-III-P4-ID-PCE-2020-0818  (REPAIR). The authors gratefully acknowledge the  National Cancer Institute (NCI)  for the biological evaluation of compounds on their 60-cell panel. The testing was performed by the  Developmental Therapeutics Program, Division of Cancer Treatment and Diagnosis  (the  URL  to the Program's website: http://dtp. cancer. gov,accessed  12 December 2022 ). The authors also thank the  CERNESIM Center  within the  Interdisciplinary Research Institute at "Alexandru Ioan Cuza" University of Iasi, Romania  for the infrastructure used in recording NMR experiments.
Ethics, Conformity to Publication Reporting Guideline, and Data Availability This study complied with the Declaration of Helsinki. Approval was obtained from the IRB Ouest II, Angers, RCB identification number: 2019-A02464-53; SI/CPP identification number: 19. 09. 17. 46723 (2019/79). This Institutional Review Board (IRB) validated the following statement written in the research protocol: "the fact that patients answer the questionnaire, after being informed in writing (information sheet placed in the office and explanatory leaflet given to the patient) and orally, will be considered as consent. There will therefore be no written consent to sign. " This study conforms with the STROBE guideline. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Acknowledgements We thank  Professor Ahcène Boumendgel  and  Doctor Yung-Sing Wong  for their interests and supports in this work. Financial support from  NeuroCoG IDEX UGA  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ), the  Labex Arcane CBH-EUR-GS  ( ANR-17-EURE-0003 ) from  CNRS  and  Université Grenoble Alpes , and a  doctoral fellowship  (to S. M. ) from the  French Ministry  are gratefully acknowledged. We also thank  Justine Magnat  for the technical support of this study and  Hervé Pointu ,  Soumalamaya Bama Toupet  and  Charlène Caloud  for the management and the maintenance of Xenopus and acknowledges the platform supported by  GRAL , financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche)   CBH-EUR-GS  ( ANR-17-EURE-0003 ).
Background: The introduction of new drugs that increase the usage of repurposed medicines for managing drug-resistant tuberculosis (DR-TB) comes with challenges of understanding, properly managing, and predicting adverse drug reactions (ADRs). In addition to the health consequences of ADRs for the individual, ADRs can reduce treatment adherence, thus contributing to resistance. This study aimed to describe the magnitude and characteristics of DR-TB-related ADRs through an analysis of ADRs reported to the WHO database (VigiBase) in the period from January 2018 to December 2020. Methods: A descriptive analysis was performed on selected reports from VigiBase on the basis of medicine-potential ADR pairs. The ADRs were stratified by sex, age group, reporting country, seriousness, outcome of the reaction, and dechallenge and rechallenge.
Discussion The aim of the present study was to evaluate the magnitude and characteristics of DR-TB-associated ADRs through an analysis of the ADRs reported in the global pharmacovigilance database (VigiBase). It was the first of its kind to be carried out at the global level using VigiBase ®. Studies conducted previously on the safety of DR-TB focused either on a few medicines or were extracted from few countries' PV databases, without considering the wider scope of DR-TB medicines used in different countries. In summary, the most relevant results found in this analysis were that half of the ADR reports of DR-TB medicines were attributed to pyrazinamide, ethionamide, cycloserine, bedaquiline, clofazimine, and linezolid. Among the most frequently reported ADRs, there were a few serious conditions, such as peripheral neuropathy or prolongation of QT (most cases were.
Discussion The aim of the present study was to evaluate the magnitude and characteristics of DR-TB-associated ADRs through an analysis of the ADRs reported in the global pharmacovigilance database (VigiBase). It was the first of its kind to be carried out at the global level using VigiBase ®. Studies conducted previously on the safety of DR-TB focused either on a few medicines or were extracted from few countries' PV databases, without considering the wider scope of DR-TB medicines used in different countries. In summary, the most relevant results found in this analysis were that half of the ADR reports of DR-TB medicines were attributed to pyrazinamide, ethionamide, cycloserine, bedaquiline, clofazimine, and linezolid. Among the most frequently reported ADRs, there were a few serious conditions, such as peripheral neuropathy or prolongation of QT (most cases were attributed to linezolid and bedaquiline).
Strengths and Limitations of the Study The strength of this study is the use of VigiBase as a data source, which included reports submitted by multiple countries. This makes it a valuable resource for identifying safety concerns with medications that may not be apparent in any one country. However, there are also some limitations. Firstly, VigiBase only includes data from reports submitted by the WHO's PIDM countries. This means that there may be significant gaps in the data, particularly from countries that are not members of the WHO's PIDM. Secondly, according to Haggar et al. [44] , ICSRs from Africa make up <1% of the global total reports in VigiBase, and another study was conducted to analyze the global patterns of ADRs submitted to VigiBase over a decade, highlighting that low-income countries reported relatively more ADRs for anti-infective cases (including DR-TB cases) than highincome countries  [45].
These findings imply that low-income countries known to report ADRs for anti-infective cases are less visible in VigiBase globally; therefore, this study should be supplemented with other data sources to address this gap. Thirdly, the data in VigiBase are not always complete. Large numbers of reports were excluded because of missing information such as the ADRs' start date, the medicine's start date, and unknown indications. This appears to be a challenge globally, as studies conducted in São Paulo (Brazil), western China, the Japanese AE database, Catalonia, the Midi-Pyrénées PV center (France), and Portugal on the completeness and quality of reporting showed similar trends  [46] [47] [48]. We therefore strongly recommend that PIMD member countries and WHO-UMC should work towards improving the quality of the reports.
Lastly, Vi-giBase does not include all of the possible ADRs collected by pharmacovigilance centers, as countries decide on what information can be shared with VigiBase. Similarly, since there are no data on the exposure denominator, and only individual case safety reports (ISCRs) of suspected adverse drug reactions (ADRs) that have already occurred are collected by the WHO Programme for International Drug Monitoring (PIDM)/UMC database, it is impossible to determine the risk associated with the suspected medicines. Given these limitations, it is important to consider additional sources of information when conducting research on the safety of medicines.
Data Source This study was conducted on data obtained from VigiBase ® , which is maintained by the WHO's Uppsala monitoring center (WHO-UMC), Uppsala, Sweden. VigiBase is the WHO's only global database of the reported potential side effects of medicinal products. It is the largest database of its kind in the world, with over 32 million reports of suspected ADRs submitted since 1968 by the member countries of the WHO's Program for International Drug Monitoring (PIDM)  [49]. It is a repository of individual case safety reports (ICSR) of ADRs collected by the national pharmacovigilance centers of about 152 member countries and 23 associate members  [49]. VigiBase is linked to medical and drug classifications including terminologies such as the WHO-ART/MedDRA and WHO ICD classifications, which are vital to effective and accurate analyses  [49, 50].
Variables and Definitions The following ADR report variables were included in the data analysis: medicines, country of origin, age, patients' age group, MedDRA preferred terms (PT) and system organ class (SOC), the seriousness of the ADR, the outcome of the reaction, and action taken. Reports with unknown information for a particular variable were included in the analysis to obtain a complete panorama of the data. An adverse drug reaction (ADR) is a response to a medicinal product, which is noxious and unintended. Adverse reactions may arise from use of the product within or outside the terms of the marketing authorization or from occupational exposure. Uses outside the marketing authorization include off-label use, overdose, misuse, abuse, and medication errors  [51]. An indication was defined as a DR-TB case that led to a treatment being recommended.
Data Analysis A descriptive analysis was performed on the eligible reports from VigiBase using prespecified indicators. The ADRs were stratified by sex (male or female), age group (<18 years, 18-64 years, and ≥ 65 years), reporting country, seriousness (serious and non-serious), outcome of the reaction (fatal, not recovered/not resolved, recovered/resolved, recovered/resolved with sequelae, and recovering/resolving), and dechallenge and rechallenge. A combination of Excel and SPSS data analysis software  [53]. was utilized for data cleaning and data analysis.
Data Availability Statement: The datasets used during this study are not publicly available due to agreements between the data contributors to the database used (VigiBase) and the database's custodian. The national centers of the WHO's Program for International Drug Monitoring (PIDM) contribute data to VigiBase, and the Uppsala Monitoring Centre serves as the custodian in its capacity as a WHO collaboration center for international drug monitoring. On reasonable request, the corresponding author may make some subsets of the data available.
General Experimental Procedures The melting point was measured on an Electrothermal Engineering Ltd. serial N • 4386. The IR spectrum was recorded in KBr, on an Alpha spectrometer (Bruker, Billerica, MA, USA) spectrophotometer. Compounds were dissolved in 85 µL of their corresponding solvent in the NMR tube (Eurisotop ref. D009B) before being analyzed in the spectrometer type Bruker Avance II+ at 600 MHz equipped with a Cryoprobe TCI operating under TopSpin 3. 2. 5, data processing under TopSpin 4. 1. 3. NMR sequences: 1 H-zgcppr, COSY, HSQC, HMBC, 13 C-zgpg30. LR-and HR-ESI-MS analyzes were performed with a SYNAPT G2 HDMS mass spectrometer (Waters, Milford, MA, USA) equipped with a pneumatically assisted atmospheric pressure ionization (API) source. The sample was ionized in positive electrospray mode, electrospray voltage: 2.
16 µg/mL for extract/fractions and 50 µg/mL-0. 08 µg/mL for compounds). After an incubation period of 72 h at 37 • C, 100 µL of the SYBR Green I solution prepared in a lysis buffer (0. 2 µL of 10,000× SYBR Green I per mL of lysis buffer) consisting of Tris (20 mM; pH 7. 5), EDTA (5 mM), saponin (0. 008%; m/v), and Triton X-100 (0. 08%; v/v) was added to each well, mixed twice gently with a multi-channel pipette, and incubated in the dark at 37 • C for 1 h. Fluorescence was measured using the microplate reader Infinite M200 (Tecan) at an excitation and emission wavelength of 485 and 538 nm, respectively. These data were normalized to percent control activity using Microsoft Excel software. The data analysis was performed with GraphPad Prism 8. 0 software, fitting by nonlinear regression, and concentration-response curves were generated to determine the median inhibitory concentration (IC 50 ) for each sample.