dataesr/scientific-paragraphs-categorization huggingface dataset:
Assessment of pharmaceuticals' targets associated with MCAEs Biological (macro)molecules with which the pharmaceuticals interact directly (Online Resource 1), identified via the Drug Gene Interaction Database (DGIdb), were defined as candidate pathway elements (CPEs). The term "pharmaceutical(s)" herein employed comprises both small molecules and biologics. Small molecules comprise low molecular weight compounds of natural or synthetic origin that bind, and alter the function of biomolecules (e. g. proteins), thus regulating biological processes. In contrast, biologics account for molecules of biological origin, with high molecular weight (e. g. monoclonal antibodies, proteins), most of which do not readily cross cell membranes but may trigger messenger responses  (Samanen 2013). The nature of association of each CPE with MCAEs was further validated in PubMed, either directly or with the help of the Biovista Vizit tool, by cross-referencing CPEs with the MCAE terms (Fig.
1a ). Only targets clearly reported to have a direct association with the above-mentioned AEs were further listed in Online Resource 2. The following information was added to each CPE listed in Online Resource 2: name of the gene encoding for the CPE; name of the pathways in which the CPE participates, according to Reactome (https :// react ome. org) and WikiPathways (https ://www. wikip athwa ys. org); citation(s) of the publications comprising the rationale for retaining the CPE in the curated list; sites of target (1) PRR = a∕(a + b) ∕ c∕(c + d) (2) IC = log2((Nobserved + 0. 5)∕(Nexpected + 0. 5)) expression, assessed using "The Human Protein Atlas" and "Human Protein Reference Database" databases, divided by level of protein expression whenever applicable; pharmaceuticals associated with the CPE, according to DGIdb.
Identification of CPEs associated with immune system-related MoA Identification of CPEs associated with an immune function response was performed by querying InnateDB and ImmPort databases for all immune system-related genes, followed by their mapping in the "Interacting Genes" list (taken from DGIdb). Further manual curation of the CPEs encoded by those genes, as well as cross-referencing of CPEs listed in Online Resource 2 that were not retrieved by InnateDB or ImmPort, was performed by carefully reading the publications retrieved by the PubMed database.
Outline of an integrative approach to AE identification An illustration of the integrative approach proposed by our group is provided in Fig. 1b. For the Pathway Enrichment Analysis, lists of MCAEs (MedDRA terms in Fig. 1a ) were used to create a FAERS-based resource of drugs associated with either of the two categories of AEs. Drugs were ranked according to the number of reports containing such AEs, based on disproportionality analysis. Drug-gene interaction databases and tools (DGIdb, NCBI Gene and Biovista Vizit) were used to identify interacting genes and CPEs for each drug. CPEs with an immunomodulatory mechanism of action were retrieved from immunological databases (Immport, Innate DB). Pathway Enrichment Analysis (g:Profiler, Enrichment Map) resulted in enriched pathway networks associated with Mood and/or Cognition AEs.
Signaling pathway analysis reveals similarities and differences in the molecular mechanisms underlying MCAEs Following expert curation of the 232 genes associated with the FAERS-selected drugs, we identified altogether 120 CPEs directly associated with MCAEs: 58 associated with Mood-and 99 with Cognition-related AEs, while 30 CPEs were related to both Mood and Cognition AEs (listed in Online Resource 2). Those CPEs included several membrane-bound (e. g. dopamine, glutamate ionotropic, cholinergic nicotinic) and nuclear (e. g. vitamin D, progesterone, retinoic acid alpha) receptors, enzymes (e. g. phosphodiesterases, monoamine oxidase A/B), immune system-related components (e. g. interleukins, interferon-gamma), but also some other types of biomolecules. About a quarter of the identified CPEs (31 out of 120) represented receptors, and the modulation of some of them was previously identified in AOP databases as MIEs (e.
Consolidation of MCAEs' mechanisms via the AOP database Our query of toxicologist-curated OECD-managed databases retrieved 11 AOPs, summarized in Online Resource 6. Concerning cognitive changes, this Table shows that inhibition of NMDA receptors, binding to SH/SeH proteins involved in protection against oxidative stress, activation of ionotropic glutamate receptors or inhibition of Na + /I -symporter (NIS) trigger a set of downstream events leading to learning and memory impairment  (AOPs 12, 13, 17, 48 and 54). Binding of an antagonist to NMDA receptors (AOPs 12 and 13) or of an agonist to ionotropic receptors (AOP 48) were reported to modulate calcium influx, and either reduce BDNF levels or impair mitochondrial function, respectively, inducing cell death and/or decreased neurotransmitter release (Fig.
Marked contribution of immunomodulation to MCAEs CPEs from Online Resource 2 involved in immunomodulatory functions were also identified by combining domainspecific databases (i. e. InnateDB, ImmPort) with expertbased literature curation of the database s-retrieved data. Online Resource 2 evidences that 79 out of all the identified CPEs (representing 66% of total elements) are associated with immunomodulation, emphasizing the importance of this broad mechanism to the onset of MCAEs. These CPEs mostly include cytokines (e. g. interferon-gamma, several interleukins), and interleukin receptors (e. g. IL-2 receptor). Noteworthy, some of these biomolecules (e. g. androgen receptor, IL-2 receptor) are not expressed in the CNS, or at least not to a considerable extent.
Conclusions This work combined the power of statistical analysis of pharmacovigilance data with the refinement of expert literature curation to propose a set of CPEs consistent with a causal relationship to the onset of MCAEs, many of which are of considerable clinical importance. Pathway enrichment and cross-talk analyses, based on genes encoding for the identified CPEs, suggested a number of pathways that represent strong candidates for the observed AEs reported in FAERS that also overlap with existing AOPs. Comparison of this data with well-documented AOPs further allowed determining whether some of those targets could be considered MIEs, or how downstream a particular CPE modulation may occur in the molecular cascade of events. Additionally, we evidenced the key role played by immune modulation, particularly interleukin signaling, in MCAEs.
Though this study design was previously approved by the Food and Drugs Administration (FDA), and certainly discussed with Indivior ® before the conductance of the trial, it is very unlikely that this kind of design will be accepted by the European Medicines Agency. In Europe, it is generally required to compare a new drug, or a new galenic form, with the standards of care, that is, here, with sublingual buprenorphine. Nevertheless, clinical studies comparing Sublocade ® with actives comparators (buprenorphine without naloxone which is the most used form in our country and methadone) are announced (EudraCT Number: 2018-004460-63), as well as studies exploring the efficacy initiating Sublocade ® in subjects that were not previously treated with sublingual buprenorphine (NCT03993392). Moreover, a study conducted among 39 patients explored the PK and pharmacodynamics features of Sublocade ®  [24].
Others reported CAR mRNA in brain areas, specifically in the nucleus accumbens, caudate nucleus, and putamen  [114, 115]. In rodents, the expression of CAR was demonstrated at the mRNA or protein levels in the cerebral cortex, hippocampus, midbrain and the cerebellum  [116] [117] [118] [119]. An available human protein ATLAS dataset outlines the expression of CAR in the brain, further indicating levels in the cerebral cortex, hippocampus, amygdala, hypothalamus and the basal ganglia  [120]. To date, no specific studies have examined the impact of gender and aging on brain CAR expression. Supporting a possible role at the cerebrovascular interface, CAR mRNA and protein expression were shown in an in vitro BBB model, using human-derived cerebral endothelial cells  [121]. The functional expression of CAR, and other cognate nuclear receptors, at the BBB was demonstrated by quantifying specific downstream P450 cytochromes or MDR gene targets  [19, [122] [123] [124] [125] [126].
Size distribution and zeta potential MLLs size distribution was obtained using Mastersizer 2000 Hydro SM from Malvern Instruments S. A (Malvern, UK). Static light scattering data were transformed into size distribution using Mie theory  [22]  by adopting a refractive index of 1. 45 for lecithin and of 1. 33 for water. A small volume of sample was introduced under stirring (1500 rpm) in the dispersion unit containing deionized water. Liposome sizes were characterized in terms of their volume-average diameter D[4; 3]: = 4,3 = ∑ ∑ Eq. 1 where Ni is the total number of liposomes with diameter Di. Each measurement was performed in triplicate. Zeta potentials were measured using Zetasizer from Malvern Instrument S. A (Malvern, UK), and estimated by means of the M3-PALS (Phase Analysis Light Scattering) technique.
Patients We retrospectively reviewed the patients' database of Dijon's Clinical Cancer Center. All patients with gastrointestinal cancer who received a 46-h continuous 5-fluorouracil infusion-based protocol from March 2018 to January 2020 were included. All patients routinely underwent a pre-therapeutic blood analysis in order to evaluate their U and UH 2. Additionally, 5-FU exposure was measured by means of a single blood sample during the first cycles. Of note, some patients may have received several lines of 5-FU-based chemotherapy, and 5-FU concentrations were thus measured for each line. DPYD genotyping was performed at the clinician discretion. All genotyped patients consented for genetic analysis, but no informed consent was required for the other patients, as samples were regular. However, data used in this manuscript were recorded in such a manner that subjects could not be identified.
Data Analysis Data are described as means ± standard deviations, median [min-max], or n (%). Intra-individual variability of U and UH 2 /U ratio was evaluated by calculating, for patients with 2 measurements, the difference, expressed as a percentage, between the highest and the lowest values. Because U and UH 2 /U ratio were not normally distributed, we used a Wilcoxon matched pairs signed rank test to compare values in patients where two measurements were performed. A t-test was conducted to compare U, UH 2 /U, and 5-FU clearance by DPYD genotype. As several 5-FU concentrations were available for most of patients, we performed regression analysis as a replicate. A linear regression was conducted to analyze the relationship between 5-FU clearance and U or UH 2 /U ratio, and slopes were compared to 0 in addition to Spearman's correlation test in order to assess correlations between variables.
2. 3. Cellular imaging and High Content Analysis (HCA) After 24 h treatment, cells were fixed 10 min with 4% paraformaldehyde in PBS and permeabilized with 0. 2% Triton X-100. Plates were then incubated in blocking solution (PBS with 1% BSA and 0. 05% Tween-20) for 30 min before addition of primary antibodies. All antibodies were prepared in blocking solution. Primary antibodies (1:1000) were incubated 2 hours at room temperature. After three washing steps with PBS, secondary antibodies (1:2000) were incubated for 45 min at room temperature. Cells were then washed three times with PBS + 0. 05% Tween-20. Nuclei were stained with 1 μg/mL DAPI in PBS for 5 min for automated cell identification by HCA. Plates were scanned with an ArrayScan VTI HCS Reader (Thermo Scientific, Waltham, USA) and analyzed using the Target Activation module of the BioApplication software.
Prior to imaging, nucleus was stained with Hoechst (5 µg. mL -1 ) and the cells were washed with PBS and fixed with 4% PFA at room temperature for 5 minutes before being washed 3 times with PBS. The images were acquired with a Nikon Ti-E inverted epi-fluorescence microscope, using CFI Plan Apo × 60 oil (NA = 1. 4) or CFI Plan Apo ×20 air (NA = 0. 75) objective, and a Hamamatsu Orca Flash 4 sCMOS camera. Hoechst and HD were excited at 395 and 641 nm, respectively, using lightemitting diode (SpectraX, Lumencor). The images were recorded using NIS Elements and then processed with Icy software.
Acknowledgements This work was supported by  ERC   Consolidator grant  BrightSens  648528. XW was founded by the  China Scholarship Council PhD fellowship  ( CSC No. 201706240033 ). We acknowledge  Dr. Delphine Garnier  and  Dr. Estefania Oliva  from the  Service de Chimie Analytique (SCA)  for their assistance in the LC-MS and RMN analyses.
Our data also demonstrated that the amplitude of the cyt c effect is distinct for each CPR variant studied. Furthermore, the inhibition by cyt c is less effective with all CPR mut previously reported to support a gain in activity for the CYP they were isolated when compared with CPR wt. Overall, our results confirm that the strengths of interactions between CYP isoforms and CPR are probably different, as suggested in our previous studies  [11, 16, 18]. Structural effects of the four mutations were analyzed via molecular dynamics simulations. None of them affected drastically the stability or the secondary structures of the FD, in line with the facts that the effects of the mutations were quite specific and the effects measured rather small. Both P117H and N151D affect the electrostatic potential of the surface of the FD, albeit with opposite effects: While the histidine residue in P117H, by being involved in hydrogen bonding with either D116 or E117, slightly neutralizes the negatively charged surface, the presence of an aspartate in N151D, by adding a supplementary negative charge, increases the negative potential of the surface.
CYP Enzyme Activity Assays The catalytic activities of CYP1A2 (EROD), 2A6 (C7H), or 3A4 (DBODF), sustained by FD mutants were evaluated as reported previously, using 0-5 µM EthR (CYP1A2), 0-20 µM coumarin (CYP2A6), and 0-10 µM DBF (CYP3A4)  [11, 16]. When testing the effects of FD mutants in CYP1A2-activities (CECOD and DBODF), the concentrations ranges were 0-50 µM CEC and 0-37. 5 µM DBF. Initial velocities were measured in triplicate and plots of velocity traces vs. substrates concentrations could be fitted according to the Michaelis-Menten equation (r 2 ≥ 0. 95) to determine steady-state kinetic parameters, using GraphPad Prism 5. 01 Software (La Jolla, CA, USA)  [22, 33, 38].
Structural Analysis of CPR Mutants The structural analysis of the four mutants (P117H, G144C, N151D and G175D) was performed using the CPR FD only, obtained from the crystal structure of human CPR wt (PDB 5FA6) as the starting material. Mutations were constructed with the YASARA software (www. yasara. org). Fully hydrated molecular dynamics simulations were performed for 80 ns using a step size of 100 ps with the Yasara software suite using the AMBER14 force field at constant pressure (water density at 0. 997 g/mL) and temperature (298 K). Parameters were set as follows: Cuboid cell extending 10 nm on each side of the protein, 0. 9% NaCl and pH = 7. 4. The Bio3D suite in R was used to calculate the coordinates of the average structure from all snapshots, the root mean square deviations (RMSD), the various reported distances and ϕ or ψ angles.
Averages and standard deviations of the data was calculated with R. pKa of ionizable residues were calculated from the average structures using the AMBER force-field and the propka option of the pdb2pqr software at a pH of 7. 5. Electrostatic potentials were calculated on the average structures with the integrated module of the Pymol software (www. pymol. org) using default parameters (non-linear Poisson-Boltzmann equation, protein dielectric 2. 0, solvent dielectric 78. 0, temperature 310 K, ion charge +1 of radius 2. 0 Å and -1 of radius 1. 8 both at 0. 15 M) and plotted on the solvent excluded surfaces (Connolly surfaces). Figures were prepared with the Pymol software and R ggplot function. All modelling data is available upon request.
Funding: This research was in part funded by a joint project, funded by the  Portuguese Fundação para a Ciência e a Tecnologia  [Grant  FCT-ANR/BEX-BCM/0002/2013  as well as Grant  UID/BIM/0009/2016  of the  Research Center for Toxicogenomics and Human Health (ToXomics) ] and the  L'Agence Nationale de la Recherche  (Grant  ANR-13-ISV5-0001 ).
Molecular Pharmaceutics mixtures were added to 990 or 900 μL of demineralized water, respectively. The droplet size of each blend was assessed after 24 h of storage at room temperature to examine emulsion formation efficiency, utilizing a Zetasizer Nano ZSP (Malvern Instruments, Worcestershire, UK). The area of emulsion formation was evaluated using pseudoternary phase diagrams as described previously with minor modifications to identify suitable SEDDS. 16 Diagrams (Figure  1 ) were mapped using the software Triplot version 4. 1. 2. In order to be able to quantify each cosolvent and to emulsify SEDDS in low dilutions with water (1:10) in the dialysis tubes (Float-A-Lyzer, Fischer Scientific, Schwerte, Germany) SEDDS needed to comprise high amounts of the specific cosolvent. Thus, SEDDS were selected from the 1:10 diagrams and prepared as described above.
The matrix Molecular Pharmaceutics volume, used for the prefilling, was between ∼120 and 200 μL (depending on the formulation and its relative viscosity) corresponding to ∼5-8 times the total capillary volume. In order to investigate the fate of the cosolvents, the formulations containing cosolvents were injected and mobilized with the formulation without containing the cosolvent of interest. Therefore, the microdroplets of all formulations were fluorescently marked with Lumogen red F300 (BASF) to confirm their presence and to determine their size. The solutes were monitored by UV absorbance at 218 nm (DMSO) and 254 nm (BA) and by fluorescence with an excitation at 480 nm. Emission light was measured through a ball lens and a high-pass filter in the wavelength range from 515-760 nm. The Taylorgrams were recorded with the Agilent Chemstation software (Agilent Technologies, Santa Clara, USA).
The latter is obtained by fitting the experimental data with eq 1. The molecular diffusion coefficient D of the sample can be calculated using eq 2 and its hydrodynamic radius R h calculated using the Stokes-Einstein equation, eq 3: σ = D R t 24 c 2 0 2 (2) Equations 1 and 2 are valid when two conditions are fulfilled. First, t 0 should be much longer than the characteristic diffusion time of the solute in the cross section of the capillary, i. e. , t 0 ≥ 1. 25 R c 2 /D for a relative error ε on the determination of D lower than 3%. Second, the axial diffusion should be negligible compared to convection; i. e. , the Peclet number P e = R c u/D should be higher than 40 for ε lower than 3%, where u is the linear velocity. 22 ] [26] [27]  2. 4. Impact of Cosolvents on Drug Payload. The impact of cosolvents on drugs was evaluated by analyzing the maximum solubility of the model drug quinine in each excipient used for SEDDS as well as in the final SEDDS preconcentrates with or without cosolvents.
Acknowledgements The authors acknowledge the European Union's  Horizon2 020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement no. 675071f or funding. UniversitØ Paul Sabatier-Toulouse,I NSATand CNRS  are also thanked for financial support. We acknowledge  HPCs CALcule nM Idi-PyrØnØes (CALMIP-OLYMPE ,g rant  P1415 ) and the  Grand EquipementN ational de Calcul Intensif( GENCI-TGCC , grant  6211 ) for generous allocationso fc omputer time. We also thank  CØline Cholletf  or analytical support,  Dr. B ernard Rousseau  for helpful discussions and  Dr. T imothØeN  aret for performing control experiments. We acknowledge  Dr. K aren Hinsinger , D r. Emilie Nehlig  and  Dr. Christophe Dugave  for their support in managing the  ISOTOP-ICS  project. Further, we thank  Dr.
3 × 10 -7 mL. Acquiring mosaics instead of single images allows to visualize bigger volumes of matrix and to decrease the detection threshold per gram. For commercial matrices, a mosaic was acquired in three well for each experiment and experiments were performed in duplicate (1350 images in total for each condition). For model matrices, a mosaic was acquired in 5 wells for each experiment and experiments were performed in triplicate (3375 images in total for each condition). The percentage of surface fluorescence (SF) for each image was calculated by binarizing the images using the MaxEntropy function in an automatic macro executed in the ImageJ software, (National Institutes of Health, Bethesda, Md. , USA)  [32]. In our experimental conditions, the range of SF that can be visualized on one image is between 0.
Bacterial Spatial Distribution in Commercial Emulsions We assessed the spatial distribution of S. aureus CIP 4. 83 in six commercial cosmetic matrices of various viscosities by CLSM imaging to identify the formulas in which uniform homogenization is not complete. Figure  2A ,B shows the frequency distribution of surface fluorescence (SF), the mean and the variance for two of the six matrices taken as an example. For matrix 1, the variance of the SF was inferior to the mean, which indicates a bacterial regular distribution. Figure  2C  illustrates the regular distribution in matrix 1 showing one series of 225 images. Matrices 2 to 5 showed a similar regular distribution with SF means between 0. 03% and 0. 45% (data not shown). The matrices 1 to 5 had low viscosities, with penetration values superior to 32 mm.
Bacterial Spatial Distribution and Enumeration in Matrices with Thickener From the composition of matrix 6, several assays were conducted to identify the component that has impaired the regular distribution of S. aureus in the matrix. It appears to be the acrylate copolymer (data not shown). To confirm the impact of its presence, we formulated two emulsified model matrices, without or with acrylate copolymer at 1. 6% and evaluated the ratio of recovery between the initial inoculum and by plate count (Figure  3A ) or by M-CLSM enumeration (Figure  3B ). Bacterial enumeration for both methods was significantly more variable in matrices with 1. 6% acrylate copolymer than in those without acrylate copolymer (p < 0. 05). We can note a higher variability for enumeration by CLSM imaging than by plate count.
Acknowledgments: The zeta potential measurements were performed at the  DIVVA platform (AgroSup Dijon ,  Université de Bourgogne-Franche-Comté. We thank  Elena Exposito-Garcia  and  Aurelie Trainoy  ( Laboratoires CLARINS ) for their expertise in cosmetic formulation,  Julien Deschamps  ( INRAE, Micalis Institute ) for his support in the CLSM analyses and  Yasmine Dergham  for proofreading the article.
Flow cytometry analysis. After washing and counting, HepaRG cells (5×10 5 cells/50 µl) were incubated for 20 min at 4 °C with 10 µl (dilution 1:5) of FC Blocking Reagent (MACS ® ). After washing, cells were incubated 30 min at 4 °C with indicated antibody or the corresponding isotype controls in PBS/serum/0. 01% azide to reduce the nonspecific uptake of mAbs. The following antibody was used: anti-TLR4-PE (TF90). Samples were acquired using a BD FACSCanto II (BD Bioscience, San Diego, CA, USA) flow cytometer and data were analyzed using FlowJo software (V10. 0. 7). Immunofluorescence. Immunofluorescence of HMGB1 was performed on HepaRG cells untreated. Briefly, cells were fixed with paraformaldehyde 1%, permeabilized with triton 0,4%, stained for HMGB1 (green) and DAPI (blue) and imaged by confocal microscopy (magnification x600).
Statistical analysis. Statistical analyses were performed using SPSS Statistics 18. 0 (Chicago, IL, USA). Differences between groups were assessed using the Kruskal-Wallis, the Mann-Whitney U or Student's t test, as appropriated. Rates of mouse survival were assessed using the Kaplan-Meier method and compared using the log-rank test. Results are expressed as mean ± SEM. A p-value of <0. 05 was considered as statistically significant.