dataesr/scientific-paragraphs-categorization huggingface dataset:
Risks associated to drugs These studies complement the role of alert and identification of adverse drug reaction profile played by spontaneous reporting and pharmacovigilance stricto sensu. Pharmacoepidemiology quantifies risk at the population level  [2, 44]. Following the pioneer pharmacoepidemiological work measuring the risk of pulmonary arterial hypertension with amphetamine appetite-suppressant drugs  [45] , several studies were performed and pharmacoepidemiological studies are now part of any pharmacovigilance survey  [44, 46]. The analysis of the methods used to withdraw drugs in France over 10 years clearly shows the increasing importance of pharmacoepidemiology methods in these decision-making  [47] [48] [49]. More recently, the importance of coupling signal detection methods in large databases, such as pharmacovigilance ones, with some pharmacological (pharmacodynamic or pharmacokinetic) characteristics of drugs (receptor affinity, for example) allowed to investigate in humans, the mechanism of adverse drug reactions.
Mode-of-action studies at higher concentrations (8-13 mM) in Caco-2 monolayers also allude to a paracellular mechanism involving activation of membrane-bound phospholipase C  [33] [34] [35]. The resulting increase in inositol 1,4,5-triphosphate (IP3) leads to an increase in intracellular calcium (Ca 2+ ), which in turn activates calmodulin and myosin light-chain kinase (MLCK). This event triggers the contraction of the peri-junctional actomyosin ring (PAMR)  [36] , permitting increased tight junction (TJ) permeability. Nonetheless, these studies involving pharmacological inhibitors are not definitive proof of a discrete paracellular effect. Impedance spectroscopy in HT29/B6 human intestinal monolayers also supports the dataset showing that C 10 acts via a paracellular mechanism; this was associated with removal or redistribution of the TJ proteins claudin 5 and tricellulin  [37].
c. dose of 15 IU in fasting subjects  [65]. Such large variability would not be acceptable for this low-therapeutic-index drug. A third related Eligen ® carrier, 8-(N-2-hydroxy-5-chloro-benzoyl)-amino-caprylic acid (5-CNAC), was also evaluated by Novartis (Switzerland) and Nordic Biosciences (Herlev, Denmark) in three large Phase III trials for oral sCT: two for osteoarthritis  [66]  (NCT00486434 and NCT00704847) and one for osteoporosis  [67]  (NCT00525798). The dosing to several thousand patients across the three trials comprised one tablet (0. 8 mg sCT with 200 mg of 5-CNAC) in a tablet administered twice a day with 50 mL of water approximately 30 min ahead of meals. These studies lasted 24 months for the osteoarthritis trials and 36 months for the osteoporosis trial. Although these trials missed their primary efficacy end-points, interesting assessments concerning dosing formats and regimes were published with conclusions that may have relevance for SNAC trials  [68].
Description Parameters Comment Reference Trial in healthy subjects 1  taking omeprazole (n = 54) (NCT02249871). 5 mg for 5 days, followed by 10 mg for 5 days) ± 40 mg omeprazole. AUC and stomach pH slightly higher in semaglutide/omeprazole group, but no need to change dose regime. [74] PIONEER-6 Phase IIIa assessed cardiovascular (CV) risk in T2D patients (n = 3183) (NCT02692716). Primary end-points: reduction in major CV events over median 16-month period. Cardiovascular (CV) outcomes not different from placebo, but suggestion of a mortality benefit of oral tablet. [75] 1. All studies in T2D patients except for the omeprazole study.
It seems that dilution, spreading, and rapid intestinal absorption of both C 10 and SNAC prevents prolonged exposure in vivo. There is no direct evidence, even from studies with a duration as long as six months, that stomach or duodenal ulcers are caused by these PEs, nor that pathogens can gain entry across a compromised intestinal epithelium. Still, post-marketing surveillance will provide more safety data in the context of daily administration over several years, at least in the case of SNAC with both vitamin B 12 and semaglutide in the event of Food and Drug Administration (FDA) approval. Additional safety experience for SNAC will be ascertained from extensive Phase III oral semaglutide trials, which will fully report in 2019. In terms of preclinical safety data, the experience for both molecules is extensive.
Background: Automated unit-dose dispensing system (ADS) of oral solid medication is a complex and sensitive production process in the drug distribution system. Hence, an appropriate training of hospital pharmacy technicians (HPT) is essential. A basic training (observational and practical) of 3 weeks is first organized to evaluate the HPT, followed by an interview with both the pharmacist and the HPT chief. The importance of the human factor (daily routine and repetitive tasks) showed by the risks analysis within this process has led us to search new learning methods to keep the team engaged. An e-learning training was selected in order to further complete the HPT initial training but also as a continuous training to the HPTs who are already working in the ADS process. Process and Methods: The e-learning tool was developed using the Google ® platform and includes four theoretical training modules (Google ® Slides): Module 1 "General organization", Module 2 "Automatic preparation machine", Module 3 "Repackaging" and Module 4 "Non-automated drug dispensing".
A recent EMCDDA study has shown that not all laboratories have the capacity to detect the more uncommon substances  [3]. Thus, the prevalence of these substances in opioid-related poisonings and deaths is most likely underreported. In the actual context of opioids crisis, more reliable and sensitive methods dedicated to the identification and quantification of NSOs in biological and non-biological matrices are needed. Recently, these analytical methods often based on chromatography (liquid or gas) coupled with mass spectrometry (MS) detection were extensively reviewed  [7, 8]. Even if targeted screening is highly sensitive and specific, they remain limited by the unavailability of reference standards  [7, [9] [10] [11] [12]. A new approach based on untargeted screening by liquid chromatography-high-resolution mass spectrometry (LC-HRMS) using a shared MS spectral database and without a need for reference standards has emerged in recent years  [13] [14] [15].
5kV. Capillary temperature was set at 350°C. Nitrogen (Nitrox UHPLCMS 18, nitrogen generator; Domnick Hunter, Villefranche sur Saône, France) was employed as sheath gas at 35 arbitrary pressure unit. The argon gas collisioninduced dissociation was used with a pressure of 1. 5 mTorr. Data were collected in selected reaction monitoring (SRM) mode, with two m/z transitions per analyte. Data acquisition was performed using Xcalibur and LC-Quan softwares (both ThermoFisher). Retention times, SRM transitions with corresponding collision energies for the 17 screened NSOs and their ISs are shown in Table  1.
This patient was a heroin addict and had already experienced different types of drugs, including NPS. Methadone substitution treatment was, therefore, initiated. The powder consumed just before the overdose was subjected to analysis. Two locks of hair were sampled 1 month (M1) (length: 6 cm) and 1 year (Y1) (4 cm) later to monitor his exposure to heroin, NSOs, and other drugs of abuse. Hair samples were cut into 2-cm segments: M1: A 0-2 cm, B 2-4 cm, C 4-6 cm; Y1: A 0-2 cm, B 2-4 cm. The segment A was the closest to the root (proximal) and reflects M1, the overdose time-frame. Each segment reflects a 2-month exposure period based on average hair growth rate of 1 cm/month  [18]. Both hair samples were analyzed by the present LC─MS/MS method and by a previously published LC─HRMS method which used the mzCloud shared database containing 81 fentanyl analogs and several non-fentanyl NSOs (e.
Results and Discussion To identify potential targets of CBG and CBC that could be of therapeutic interest, a 3D ligand-based virtual screening was first performed within the DrugBank database  [18]. Indeed, this approach has already demonstrated to provide valuable results for drug repurposing, allowing the identification of structurally unrelated compounds with similar bioactivities  [19]. Among the available databases, the DrugBank was selected because it provides a comprehensive list of approved and investigational drugs, with trustful bioactivity annotations on relevant therapeutic targets. The DrugBank was first prepared for the 3D ligand-based similarity analyses (see "Materials and Methods" section for details). Then, the DrugBank compounds were subjected to a 3D similarity screening against the generated CBG and CBC multi-conformer queries.
Regarding CBC, the n-pentyl-chromene-5-ol group provided a less favorable overlap with the 5-pentyl-2-phenoxy group of 5PP, this moiety occupying significant larger volume with respect to the hydroxyphenyl group of 5PP. Good ligand-based alignments of CBC and CBG with other cannabinoids reported in the DrugBank database were also found (see Table  S1  in the Supporting Information), such as cannabidiol (DrugBank ID: DB09061) and dronabinol (DrugBank ID: DB00470), which have recently been approved for treating epilepsy in children and nausea associated with cancer chemotherapy, respectively  [21, 22]. However, we decided to focus our attention to the InhA enzyme, which is a validated target of well-known antitubercular drugs  [23, 24]. Although CBC and CBG showed good shape similarity with the 5PP inhibitor, the two compounds were also docked in the InhA crystal structure (PDB code: 2B36)  [20] , as described in the "Material and Methods" section.
The different binding mode likely originates from steric repulsion of the 2-(4-methylpent-3-enyl) moiety of CBC with the Although CBC and CBG showed good shape similarity with the 5PP inhibitor, the two compounds were also docked in the InhA crystal structure (PDB code: 2B36)  [20] , as described in the "Material and Methods" section. This analysis made it possible to assess whether the investigated compounds also possess good steric and electrostatic complementarity with the InhA binding site. The 2B36 crystal structure was preferred among others available for InhA, because the enzyme is in complex with 5PP  [20]. Indeed, the selection of suitable receptor conformations for docking by means of the similarity between the crystallographic and the screening ligands is among one of the most used methods to improve structure-based virtual screening results  [9, 26].
Ligand-Based Virtual Screening on the DrugBank Database Compounds with associated bioactivity annotations were first retrieved from the DrugBank database (www. drugbank. ca, accessed on April 20, 2018)  [18]. Then, compounds with recognized toxic and reactive functional groups or transition metals, and a molecular weight lower than 150 or higher than 850 Da were removed, yielding a database of 6014 unique compounds. The FILTER software (OpenEye, Santa Fe, Mexico) was used for the filtering calculations  [40]. Afterwards, all combinations of stereoisomers, ionization states and tautomers potentially present at neutral pH were generated for both the investigated cannabinoids and the filtered DrugBank ligands by using the Quacpac python toolkits  [41]. Pre-defined chiralities were kept unaltered and pre-treated structures were minimized according to the MMFF force field  [42].
Finally, up to 10 and 600 conformers were generated for the investigated cannabinoids and the filtered DrugBank compounds, respectively. A cutoff of 0. 5 Å on RMSD and an energy window of 10 kcal/mol were used as parameters to accept conformers during the conformational sampling with the OMEGA software  [40, 43]. A multi-conformer versus multi-conformer 3D shape-based virtual screening was performed to evaluate the similarity profile of the investigated cannabinoids with respect to the filtered DrugBank compounds. The ROCS software (version 3. 2. 1. 4) was used as the engine for the similarity calculations  [25, 44]. Finally, ligand alignments were visually inspected, and the activity annotations of the DrugBank compounds were carefully analyzed.
Structure-Based Virtual Screening on Enoyl Acyl Carrier Protein (ACP) Reductase The InhA crystal structure (PDB code: 2B36) was first downloaded and pre-processed with the Protein Preparation Wizard module available within the Schrödinger Suite 2018-3  [20, 45]. In particular, atom types and bond connectivity issues in the downloaded crystal structure were fixed. Moreover, missing side chains were rebuilt by using Prime (version 5. 3)  [46]. Then, hydrogen atoms were added to the pre-treated crystal structures and their coordinates were energy-minimized. Finally, ions, solvent and water molecules were removed. Docking calculations were performed by using both Glide (version 8. 0. 012) and the Induced Fit Docking protocol available within the Schrödinger Suite (New York, NY, USA) 2018-3  [27, 28].
Supplementary Materials: The following are available online at http://www. mdpi. com/1420-3049/24/14/2567/s1. Figure  S1 : superimposition of the 5PP binding modes predicted by Glide and the Induced fit protocols with its crystallographic conformation; Figure  S2 : superimposition of the predicted ligand-based and structure-based (with Glide) poses of CBC; Figure  S3 : CBG and CBC binding modes predicted by the Induced Fit Docking protocol; Figure  S4 : the dose-response curve of CBG on InhA; Table  S1 : similarity values obtained with ROCS of CBC and CBG with respect to the DrugBank ligands. Author Contributions: The manuscript was written through contributions of all authors. All of them have given approval to the final version of the manuscript. G. R. conceived the study and performed the computational structure-based calculations; L.
Formulations A and B displayed similar hydrodynamic diameter and PDI, which were consistent with previously described studies  [11]. Formulations C and D showed larger hydrodynamic diameter, and larger PDI for Formulation D. These differences may be due to the higher amount of SA-PEG 100 -GalNAc, which polymer chain presents a 2. 5 fold larger number of ethylene oxide units than Myrj TM S40  [11]. This hypothesis was confirmed by data of Formulation E, comprising the same ratio of Myrj TM S100 long-chain surfactant. Zeta potential values were not significantly different: All nanoparticles presented surface charge close to neutrality, with a slightly negative value (presence of small quantities of hydrolyzed fatty acids in wax and oil components). The long-term colloidal stability of formulations A-E was evaluated by the measurement of hydrodynamic diameter and PDI over storage time.
Dynamic Light Scattering The hydrodynamic diameter and zeta potential of NLC were measured at 22 • C with a Malvern Zeta Sizer Nano instrument (NanoZS, Malvern, Orsay, France) in 0. 1× PBS buffer. Physical stability was investigated by DLS measurements over one year with samples stored at 4 • C. At least three different NLC batches (2 mL, lipid dispersed phase weight fraction: 10%) were used per condition. Mean average diameters and polydispersity indices reported were obtained from scattered light intensity results. Data were expressed in terms of mean and standard deviation of all the samples for each condition, each sample result being the mean of three independent measurements performed at 25 • C.
Funding: This work was supported by the  Région Auvergne Rhône-Alpes  (Ph D grant for L. G. ) and the  French National Research Agency  in the framework of  Arcane Labex ,  CBH-EUR-GS  ( ANR-17-EURE-0003 ), and  Glyco@Alps  " Investissement d'avenir" program  ( ANR-15-IDEX-02 ).
, 2017; Nordström et al. , 2018). The safety and efficacy of 1% DPK-060 in a polyethylene glycol (PEG)-based ointment has been studied in a clinical phase II trial in the treatment of skin infections in atopic dermatitis patients. The results of this trial revealed that DPK-060 1% ointment was well tolerated by the subjects and significantly reduced the microbial density in eczematous lesions after 14 days with twice daily application compared with placebo (ClinicalTrials. gov Identifier: NCT01522391;  EudraCT: 2007-007103-32). This study applies new formulation strategies including different classes of nanocarriers to formulate DPK-060 for topical delivery, and characterizes these different dose formulations in terms of preclinical in vitro, ex vivo, and in vivo antimicrobial activity (see Figure  1  for a schematic overview of the study).
aureus, which is one of the bacterial species most frequently causing human wound infections  (Cardona and Wilson, 2015). We found that DPK-060 formulated in poloxamer gel caused a marked and statistically significant reduction in microbial counts with a clear dose-response relationship both ex vivo and in vivo. These data are consistent with the results of the previously completed clinical trial where total microbial count as well as count of Coagulase Negative Staphylococci (CoNS) and Gram-positive bacteria were significantly lower in eczematous lesions of patients with atopic dermatitis after 14 days of treatment with DPK-060 1% ointment (ClinicalTrials. gov Identifier: NCT01522391; EudraCT: 2007-007103-32). Protection against proteolytic degradation by infectionaffiliated enzymes has earlier been reported in in vitro studies when different AMPs were encapsulated into LNCs or cubosomes  (Boge et al.
However, we found that the release profile for DPK-060 was similar in poloxamer gel with or without encapsulation of the peptide into LNCs (about 40% of DPK-060 was released within 3 h) and the reason for low efficacy of DPK-060-loaded LNCs in vivo remains elusive. We did not observe any significant reduction in cell viability with DPK-060 in any nanocarrier formulation tested when assessed by in vitro EpiDerm Skin Irritation Test. Furthermore, we found no visible signs of systemic toxicity or local irritation in murine surgical site infection model in connection to the administration of DPK-060-containing formulations. This is consistent with the results of the clinical trial, where no serious adverse events (SAE) were reported after dermal application of DPK-060 in atopic dermatitis patients, whereas adverse events (AEs) were observed at similar frequency in the DPK-060 and placebo groups (ClinicalTrials.
gov Identifier: NCT01522391). In conclusion, the present study confirms that DPK-060 has the potential to be an effective and safe drug candidate for the topical treatment of microbial infections; however, under the applied test conditions, the adsorption of the peptide to nanocarriers failed to show any additional benefits. Notably, in this study, a single administration of the test formulations, combined with a short follow-up time, was used both in ex vivo and in vivo assessments, which is considered a limitation of the experimental design. Repeated application over several days, and a longer follow-up period, would closer resemble the clinically relevant situation and may lead to different results in terms of both efficacy and safety.
ACKNOWLEDGMENTS  Jessica Eriksson ,  Karin Hallstensson ,  Karin Agrenius , and  Ronja Widenbring  ( RISE Research Institutes of Sweden AB ),  Viviane Cassisa  ( Laboratoire de Bactériologie, CHU Angers, France ),  Matthieu Eveillard  ( Equipe ATIP AVENIR, CRCINA, INSERM, Université de Nantes, Université d'Angers, Angers, France ), and  Nada Matougui  ( 3MINT ,  UNIV Angers, INSERM Université Bretagne Loire, Angers, France ) are gratefully acknowledged for technical support.
Examples of state-of-the-art software solutions based on expert systems are MetabolExpert  (Darvas, 1987) , META  (Klopman et al. , 1994 (Klopman et al. , , 1997;; Talafous et al. , 1994)  and METEOR  (Button et al. , 2003; Marchant et al. , 2008). More recently, MetaPrint2D-React proposed a new approach based on data mining and statistical analysis  (Adams, 2010). MetaPrint2D-React uses circular fingerprints of each atom of the queried molecule. Predicted metabolites are generated by applying biotransformation rules based on fingerprint similarity. One of the main drawbacks of the knowledge-based expert systems is the combinatorial explosion of predictions, leading to a high number of false positives  (Bugrim et al. , 2004). In order to improve precision while limiting the loss of sensitivity, cutoffs and probability levels are implemented.
Tarcsay et al. (2010)  used first the software predicting xenobiotic metabolites, Metabo-lExpert  (Darvas, 1987)  and next the docking program GLIDE  (Friesner et al. , 2004; Halgren et al. , 2004)  to increase the precision. Similarly,  Piechota et al. (2013)  developed a method combining metabolites prediction by Metaprint2D-React  (Adams, 2010)  and SOM prediction by SMARTCYP  (Rydberg et al. , 2010). Such hybrid approaches aim to reduce the number of over-predicted metabolites. However they remain unsatisfactory for predicting large maps of biotransformation metabolism either because of their low sensitivity or because of their poor scalability since docking-based methods are very time-consuming. In this present work, we developed an original integrative method to accurately predict and study the different routes of transformation of HAAs into toxic and non-toxic compounds.
0  (Dang et al. , 2016)  and Xenosite Reactivity 2. 0  (Hughes et al. , 2015 (Hughes et al. , , 2016)). Finally, we used predictions from Xenosite Reactivity 2. 0  (Hughes et al. , 2015 (Hughes et al. , , 2016) )  to identify metabolites that are reactive with DNA. In order to combine these different tools, we introduced score-based criteria to accurately estimate enzyme specificity and DNA-reactivity. The tools were calibrated using manually curated compound databases independent from HAAs. The predictions of the method applied to three referenced HAAs demonstrate that combining different approaches and parameterizing them using manually curated knowledge leads of a high gain of sensitivity without any loss of precision. When applying our method to the 27 unknown HAA, we generated maps for the metabolic biotransformations of each HAA, including new metabolites with a high predicted DNA reactivity, which can be further explored through a userfriendly and interactive web interface.
Software To study HAA metabolism and predict the metabolites leading to DNA adducts, we implemented a workflow combining a set of software tools including Metaprint2D-React  (Adams, 2010) , WhichCyp  (Rostkowski et al. , 2013) , Way2Drug-SOMP  (Rudik et al. , 2015) , Xenosite Metabolism 1. 0  (Zaretzki et al. , 2013) , Xenosite UGT 2. 0  (Dang et al. , 2016) , and Xenosite Reactivity 2. 0  (Hughes et al. , 2015 (Hughes et al. , , 2016)). Way2Drug and Xenosite are combined to potentiate SOMs identification and to overcome the possible lack of SOM recognition by using a single method. The main lines of the workflow consist in generating a graph by the iterative use of Metaprint2D-React. This graph is next processed by software tools for SOM prediction (WhichCyp, Way2Drug SOMP, Xenosite Metabolism 1.
0 and Xenosite UGT 2. 0) in order to reduce over-predicted metabolites and to identify metabolites reactive with DNA (Xenosite Reactivity 2. 0). Metaprint2D-React  (Adams, 2010 ) is a software predicting metabolic transformation in human, dog and rat models using 3 configuration parameters (Loose, Default, and Strict). We use Metaprint2D-React in an iterative way. Each considered HAA is converted to a SMILES (Simplified Molecular Input Line Entry Specification) string and used as input to Metaprint2D-React. The fingerprint matching parameters are initialized as default and the human model is selected. The metabolites predicted by the tool are used as new inputs in MetaPrint2D-React with the same parameters. We iterate this procedure in order to obtain two levels of metabolites (depth = 2), meaning that the initial HAA can undergo two consecutive transformations as described in the general scheme of HAA metabolism (Fig.