dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion In this paper, we presented the LiSA approach, a deep learning based pipeline for Adverse Drug Reaction monitoring in the biomedical literature. To our knowledge, our work is the first one to rely on a modular architecture of open-source fine-tuned models and providing access to multilevel outputs (AE/Drug relationship, AE and Drug entities, ADR Seriousness monitoring). We evaluated the performance of the system at two levels a) predictive performance based on a benchmark dataset labeled by medical reviewer and made available for future research and b) user-based performance where ADR monitoring with LiSA is compared with a semi-manual work based on keyword search on PubMed search engine. We have shown that based on LiSA user interface, a medical reviewer is able to retrieve 2. 5 times more relevant documents than with a simple semi-manual search.
In addition to the genetic approach, we also tested the impact of chemical inhibition of the RIPK1 kinase activity using a generation of more specific inhibitor with better in-vivo pharmacokinetic properties, Nec-1s. As mentioned by Yi and colleagues  [1] , DMSO is known to interfere with the metabolism of APAP on its own partly improving liver injury. Therefore, our investigations with Nec-1s included the essential control of the vehicle alone, which only included 6% of DMSO. The inhibitory activity of the batch of Nec-1s used in our investigations was also verified in parallel in an in vitro assay (data not shown). Thus, WT C57Bl6/J mice received vehicle alone or with Nec-1s prior to APAP injection. The severity of the hepatitis induced in the two groups of mice was similar, as evidenced by the evaluation of serum transaminase levels and of percentages of necrotic liver tissue (Fig.
Acknowledgment We would like to thank  Dr. M. Bertrand  and  Pr. P. Vandenabeele , both from the  Inflammation Research Center, VIB (Belgium)  for the provision of the Ripk1 LPC-KO mouse strain and the team of  Peter J. Gough  ( GlaxoSmithKline, Collegeville, PA, USA ) for provision of the Ripk1 K45A mouse strain. We also extend our thanks to  Mélanie Simoes-Eugénio ,  Ghania H. Kara-Ali  and  Ghiles Imerzoukene  for their technical assistance. We acknowledge the " Laboratoire de Biochimie-Toxicologie " from the  "CHU de Rennes " for transaminase measurements. For histological analysis and animal house facilities, we would like to thank the dedicated platforms (i. e. H2P2 and animal house platforms) of  SFR Biosit-UMS 3480 ,  US_S 018 , France. J o u r n a l P r e -p r o o f.
Financial support This work was supported by the  "Contrat de Plan Etat-Région" (CPER)  grant named "Infectio"; the  "Ligue Contre le Cancer, Comités du Grand Ouest "; the  Biology and Health Federative Research Structure of Rennes (Biosit ,  UMS CNRS 3480 / US INSERM 018 ) and the  "Fondation pour la Recherche Médicale " (FRM # DEQ20180339216 ). H. H. and M. F. were both supported by a PhD fellowship from the  Government of Pakistan (Higher Education Commission)  and A. H. by a postdoctoral fellow-ship from the  "Fondation pour la Recherche Médicale ".
Funding: This research funded by public grants overseen by the  Paris Ile-de-France Region-DIM "Respore", French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  ( Labex Charmmmat ,  ANR-11-LBX-0039 , and  ANR-10-LABX-0035 ) and  CNRS. The present work has benefited from the  Imagerie-Gif core facility  supported by l' Agence Nationale de la Recherche  ( ANR-11-EQPX-0029 /Morphoscope ,  ANR-10-INBS-04 / FranceBioImaging ;  ANR-11-IDEX-0003-02/Saclay Plant  Sciences), as well as the  Small Molecule Mass Spectrometry platform of ICSN (Centre de Recherche de Gif -http://icsn. cnrs. fr(accessed on 18 November 2022)). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
The growth of the multilayered coating formation was observed by scanning electron microscopy (SEM) analysis (Figure  3 ). After eight layers, the LbL-MP surface looked very rough and porous (Figure  4B ), while, after the deposition of the 14 nanolayers, a more homogeneous and smoother coated surface was observed (Figure  4C ). By using ImageJ software, after the LbL functionalization, the diameter of the bare MPs (CaCO 3 only) increased from an average diameter of 1. 39 ± 0. 43 µm to 1. 52 ± 0. 32 μm. Furthermore, the successful multilayer coating manufacturing was confirmed by ζ-potential measurements and infrared spectroscopy analysis using FT IR-ATR (see Supplementary Figures  S1, S2 ). In vitro MT release, after incubation of the LbL MPs at 37 °C, was calculated by UV-Vis spectrophotometry and shown in Figure  4D.
39 μm , d p 1 1. 52 μm. ( 4. 1) That results in the following volumes (areas in 2D), respectively:  Frontiers in Bioengineering and Biotechnology | www. frontiersin. org V 0 1. 5175 μm 2 , V 1 0. 2971 μm 2. ( 4. 2) The core has been initially loaded by the encapsulated drug, with the enveloping shell free of the drug, and the respective masses are as follows: M 0 2. 5 • 10 -4 μg , M 1 0. (4. 3) To solve the mathematical problem in Eqs 2. 1, 2. 2, and in view of more complex geometries, we employ the finite element method by using COMSOL Multiphysics software  (COMSOL Multiphysics, 2019). The FEM triangular mesh has a maximum size of 0. 16 µm which guarantees a convenient resolution. Further refining of the mesh did not alter the solution (grid independence) (Figure  4 ). The simulation has been run over 400 h, with a discretization time step automatically adapted.
Screening of Databases Our research strategy for finding new substrates for CYP2U1 was based on a bioinformatics approach carried out through molecular modelling studies using a virtual screening of HMDB, Cayman and Lipid Maps-Lipidomics databases on our 3D homology model of truncated CYP2U1  [10]. Further details on the choice of molecules are indicated in Materials and Methods (Section 4. 4), and complete data of this study will be published elsewhere. Among the best promising compounds, we selected LTB4 as a putative substrate for CYP2U1 because of its important biological properties.
Screening of Databases Our research strategy for finding new substrates for CYP2U1 was based on a bioinformatics approach carried out through molecular modelling studies using a virtual screening of HMDB, Cayman and Lipid Maps-Lipidomics databases on our 3D homology model of truncated CYP2U1  [10]. Further details on the choice of molecules are indicated in Materials and Methods (Section 4. 4), and complete data of this study will be published elsewhere. Among the best promising compounds, we selected LTB 4 as a putative substrate for CYP2U1 because of its important biological properties.
u. ; auxillary gas flow, 5 a. u. Mass spectra were recorded with a resolution of 1 a. m. u. , with a frequency of 20 Hz. The range of masses scanned for the total-ion chromatogram was m/z 120-600. For all products, the indicated molecular ions corresponded to M-H. After data acquisition, HPLC-MS chromatograms and UV-Vis spectra (230-550 nm) were analyzed with Xcalibur 2. 2. 0 software (Thermo, Les Ulis, France).
Computational All computations were performed as previously described  [10, 23]  using the software package Biovia Discovery Studio 2016 (Dassault Systems, Cambridge, UK). The parameters applied for the heme (defined as Fe III protoporphyrin IX) were obtained from Oda et al. [35]. A 3D model of CYP2U1 deprived of its predicted membrane-spanning domain (residues 57 to 544) was used as previously described  [10, 23]. Each of the three databases: Human Metabolome Database (HMDB, https://hmdb. ca/), ~2400 molecules, metabolites found in human); Cayman Chemical (https://www. caymanchem. com/), ~5000 molecules, predominantly lipids and eicosanoids); and Lipid Maps-Lipidomics (https://www. lipidmaps. org/) ~4500 molecules, fatty acids, glycerolipids) was downloaded. The compounds were first totally ionized (6.
5 < pH < 8. 5) to make them electrically neutral. Then, the tautomers and conformers for each molecule were generated and the ligands were screened on the CYP2U1 3D model. Poses were generated and their analysis allowed the selection of ligands according to their CDOCKER interaction energies (~-80 to -50 kcal/mol)  [22]  leading to compounds with a better affinity for the active site. Then, the positioning of the molecules close to the active site and the distance between the heme iron and oxidizable atoms (distance < 5 Å) were checked to select the most interesting compounds. From this study, 32 compounds were selected from the HMDB; 80 from Cayman and 130 from Lipidmaps. Manual analysis of the data resulted in the selection of ~10 compounds of high pharmacological interest. Among them, LTB 4 appeared as an important target due to its key roles in inflammatory disorders.
| Data collected The medical dictionary for regulatory activities (MedDRA) was used to define ADRs by system organ class (SOC) and preferred term (PT). Vaccinees were asked to report the intensity of ADRs as mild, moderate or severe. Serious ADRs were defined as those that resulted in death or are life-threatening, or that required inpatient hospitalization or the prolongation of existing hospitalization, or resulted in the persistence of significant disability or incapacity. Adverse events of interest (AEIs) as identified by the pharmacovigilance risk assessment committee (PRAC) were analysed separately and classified as systemic reactions (fever [≥38 C], nausea, vomiting, malaise, headache, decreased appetite, myalgia and/or arthralgia, irritability/prolonged crying [aged <5 years]), injection-site reactions (pain, erythema and swelling) and events indicative of allergic and hypersensitivity reactions, including rash and ocular symptoms.
ACKNOWLEDGEMENTS The authors would like to thank all participants who took part in the FLU000170 study and the  Sanofi and Zifo EPSS  study team members. The authors also thank  Isabel Grégoire, PhD  ( Sanofi Pasteur ) for editorial assistance and manuscript coordination. Medical writing and editorial assistance were provided by  Juliette Gray PhD , in  Science Communications ,  Springer Healthcare Ltd, UK. Funding for this assistance was provided by  Sanofi.
DATA AVAILABILITY STATEMENT The datasets generated and/or analysed during the current study, including the raw data, are not publicly available in order to safeguard the privacy of participants and the confidentiality and protection of their data, as well as protect commercially sensitive information. Qualified researchers may request access to patient level data and related study documents including the clinical study report, study protocol with any amendments, blank case report form, statistical analysis plan and dataset specifications. Patient level data will be anonymized, and study documents will be redacted to protect the privacy of our trial participants. Further details on Sanofi's data sharing criteria, including required permissions to access the data, eligible studies and process for requesting access can be found at: https://www.
Methods: We performed a disproportionality analysis using VigiBase, the World Health Organization Pharmacovigilance Database, using the information component (IC). The IC compares observed and expected values to find associations between drugs and hyperammonaemia using disproportionate Bayesian reporting. An IC 0. 25 (lower end of the IC 95% credibility interval) > 0 is considered statistically significant. The main demographic and clinical features, confounding factors, and severity of cases have been recorded.
Causality assessment A case-non-case analysis was performed for each drug in the database, using the information component (IC). Briefly, the IC is an indicator value for disproportionate reporting when using the Bayesian method for disproportionality analysis developed by Uppsala Monitoring Center  [20]. This method compares the proportion of a given adverse event associated with a drug to the proportion of the same adverse event for all other treatments in VigiBase  [20]. We deliberately chose to use information component (IC) as the disproportionality assessment method, which is more conservative than the reporting odds ratio (ROR), in order to avoid false-positive signals and strengthen the results. The information component is obtained by the formula: where Nobserved is the number of reported cases for a given adverse drug reaction; Ndrug is the number of cases for a given drug, regardless of the adverse reaction reported; Neffect corresponds to the number of cases of a given adverse reaction, whatever the responsible drug; and Ntotal corresponds to the total number of cases in the database, regardless of the drug or adverse reaction.
Imputability assessment The proportion of cases in which no other drug was suspected, the mean number of drugs suspected, the   Only ten drugs were already described in those labels as being associated with hyperammonaemia in the FDA's and EMA's labels (B4 extrinsic imputability): valproic acid, valpromide, topiramate, asparaginase, fluorouracil, haloperidol, pegaspargase, zonisamide, deferasirox and amphotericin B. The frequency of hyperammonaemia is reported as uncommon (between 1/100 and 1/1000 cases) for asparaginase, rare (between 1/1000 and 1/10 000 cases) for valproic acid, valpromide, topiramate and undetermined for the other drugs of this group. Out of the 61 other drugs, 6 have been widely published in reference databases and/or books (B3 extrinsic imputability), 45 have been described in case-reports or case series (B2 extrinsic imputability) and 10 have never been published (B1 extrinsic imputability).
For the extrinsic imputability, B4 is quoted when the effect is expected (described in the summary of product characteristics), B3 if the effect is widely published with this drug in reference databases and/or books, B2 if cases are published in a scientific journal, B1, if the effect is not published were associated with the highest overall mortality rates. Hyperammonaemia secondary to anti-epileptic and psychiatric drugs had more favourable outcomes. This difference of outcome could be partly driven by more severe underlying comorbidities in patients with immunosuppressants and oncological treatments and the effect of the drugs themselves on mortality needs to be further characterised. Because of the absence of large series in the literature, this could not be compared to previous data, but these results seem to be in accord with the published case reports  [26, 27].
It should be noticed that drugs known to be associated with hyperammonaemia such as asparaginase, 5-fluorouracil or capecitabine had lower mortality rates (6. 9%, 4. 3% and 6. 7%, respectively) compared to more recently marketed molecules such as sunitinib, regorafenib or basiliximab (15. 4%, 18. 2% and 60%, respectively). This could suggest that the identification of the situation and its prompt management might improve the outcome and/or may indicate an awareness bias (i. e. serious cases and those involving new drugs are more likely to be reported). Tacrolimus is one of the drugs responsible for the most cases quantitatively of suspected drug-associated hyperammonaemia in VigiBase (6th out of 71) despite the absence of its mention in FDA and EMA's drug labels. These latter cases are associated with a 57% mortality, but with data available in VigiBase, determining the exact cause of death in each individual case might be challenging, due to several limitations detailed below including exhaustivity of data information reported in records.
First, it cannot be ruled out that hyperammonaemia is a dose-dependent adverse effect occurring at a "supratherapeutic" cumulative dose for some medications such as chemotherapies. Furthermore, a modification of the drug's dose might also increase the risk of hyperammonaemia. Also, many intercurrent events (infection, acute renal failure, liver failure or the instauration of another drug with potential interactions, etc. ) might cause hyperammonaemia in a patient taking one of the described drugs. For these reasons, when clinicians are faced with hyperammonaemia and after having excluded classical causes, a drug-associated origin should be suspected. The imputability of each drug should be assessed, based on an evaluation from the pharmacovigilance centre. The main limitations of our analysis are those of retrospective pharmacovigilance studies from databases.
Indeed, current post-marketing pharmacovigilance is strongly based on spontaneous notification and presents well-known bias  [21, 30]. Among these, the main bias concerns the lack of information and the under-notification. For instance, the number of cases with liver failure is strictly low and does not represent the prevalence of liver injuries in the population. This is related to the fact that hyperammonaemia is attributed-maybe wrongly-to the liver disease by hepatologists, who disregard the possibility of a drugassociated event. Due to its international nature, this database is particularly suitable for the identification of rare adverse events such as hyperammonaemia, but those results should be always confirmed by translational studies identifying the mechanisms at play  [31, 32]. Moreover, underlying conditions such as cancer Fig.
Furthermore, their uses are almost impossible here in view of the heterogeneity of the recordings and the available information (especially because of the absence of narratives). Finally, analyses on VigiBase do not systematically contain the exact values of lab results (such as ammonaemia or liver enzymes that can be absent or given as values relative to upper normal range), so interpretation of those parameters can only be qualitative. Furthermore, it is important to note that the imputability of each molecule is difficult to assess for drugs that are rarely prescribed in monotherapy, such as some chemotherapies or anti-epileptics. Furthermore, without data on numbers of exposed patients in VigiBase, this work cannot assess the incidence or risk of hyperammonaemia with these drugs. Finally, the description of the clinical presentation during hyperammonaemia should only be considered as exploratory because of the strong influence of the underlying conditions and the difficulty to attribute those symptoms to hyperammonaemia.
Integrated structure-based and machine learning modeling to predict inhibition of CYP2C9 In this study, we developed classification machine learning models for the prediction of CYP2C9 inhibition and for the identification of new drugs inhibiting CYP2C9 (Fig  1 ). We collected known inhibitors and non-inhibitors of CYP2C9 from the ChEMBL  [31]  and PubChem databases  [32]  to build training and external test datasets for ML modeling. In order to create predictive models with applicability covering drug-like molecules while maintaining chemical PLOS COMPUTATIONAL BIOLOGY diversity we performed "soft" drug-like filtering  [33]  and diversity clustering of the collected compounds (see Methods for details). The filtered dataset of the diverse clusters centroids contained 4840 inhibitors and 3301 non-inhibitors of CYP2C9.
The drugs diclofenac and losartan, typical substrates of CYP2C9, were present during the MD simulations in the substrate-bound states. In that work  [30] , we performed structural clustering using with a Root Mean Square Deviation (RMSD) distance of at least 1. 0 Å for all atoms of the binding site. The most populated 40 clusters (corresponding to the conformations covering 85% of the MD simulations) generated for each of the three studied systems, CYP2C9 apo, CYP2C9 diclofenac-bound, CYP2C9 losartan-bound, were retained. Then, molecular docking of five diverse drugs, known substrates of CYP2C9, warfarin, diclofenac, glimepiride, flurbiprofen and losartan, was performed into the MD conformational ensemble of the retained 120 centroids and into several crystal structures of CYP2C9. These docking analyses allowed us to identify the best five MD protein conformations (MD1, MD2, MD3, MD4 and MD5) with a competent substrate orientation in the active site and two crystal structures (PDB IDs 1R9O, 5XXI)  [34, 35]  (see S2 Fig and  S1  Table ) as representative conformations for key movements of the binding pocket area  [30].
The seven chosen CYP2C9 structures showed important conformational changes of the binding site allowing to accommodate diverse ligands. Here, the training set of the diverse CYP2C9 inhibitors and non-inhibitors were docked into these two crystal and five MD protein structures using AutoDock Vina software  [36]  (see Methods for details). The computed interaction energies (IEs) by docking-scoring (shown in S3 Fig) were then used (1) as descriptors for the machine learning models, and (2) as an additional filter for predicting CYP2C9 inhibitors. Physicochemical molecular descriptors of the training set's molecules were calculated using MOE software  [37]. Initially, we calculated 354 2D and 3D MOE descriptors. Highly correlated descriptors with an absolute value of the Pearson correlation coefficient greater than or equal to 0.
85 and descriptors with near null variance were removed. This selection resulted in 170 descriptors. The calculated IEs for the seven CYP2C9 structures were added as structure-based descriptors accounting for the protein-ligand interactions. Then, to avoid overfitting and to propose an in silico strategy with a reduced calculation time, we selected the best descriptors based on their relative importance in predicting the inhibiting character of a molecule. That selection comprised building a plurality of RF models on the training dataset and selecting the subset of descriptors with the highest Gini importance  [38]. The Gini index, also known as the Gini impurity index, is a measure of the probability of incorrectly classifying a randomly selected element in a dataset if it was randomly labeled according to the class distribution in the dataset.
Therefore, we further considered the best 15 and 20 MOE descriptors. In addition, the first 43 descriptors, including the 7 IE descriptors and showing an importance value > 20, were selected for further analyses (S1 Table ). A plateau was observed (S4 Fig) for importance < 20, thus taking more descriptors would add a noise in the models. The seven IEs showed importance > 20 and two MD conformations and one crystal structure (PDB ID: 1R9O) showed importance > 30. The 36 best MOE descriptors in terms of importance can be seen in S1 Table. The most important descriptors corresponded to lipophilicity (2D), the number of aromatic bonds (2D), partial charges (2D), a negative van der Waals surface area (2D, 3D), and shape (3D: first diagonal element of diagonalized moment of inertia tensor, out-of-plane potential energy, and surface roughness).
A benchmark of different datasets used and models' performances as reported in the literature is given in S5 Table. In order to directly compare the performance of our models with other recent ones, we show here the performance of our final models and two state-of-the arts models available at the web servers CYPlebrity  [44]  and ADMETlab  [45]  on our external validation test set of inhibitors and non-inhibitors (see Table  3 ). Overall, our models performed better than the others (Tables  3  and S5 ), and showed remarkably better sensitivity, which is critical to detect inhibitors. The differences between our models and CYPlebrity and ADMETlab ones are statistically highly significant as seen from Table  4. To ensure the reliability of our models, we performed experimental validation and we successfully identified new CYP2C9 drug inhibitors.