dataesr/scientific-paragraphs-categorization huggingface dataset:
Application of the predictive models for the identification of new drugs inhibiting CYP2C9 We employed the retained ML models to screen 4480 approved and experimental drugs collected from four drug databases  [46]  (see in the S1 Text for their preparation) to identify new drugs that inhibit CYP2C9. Drugs already experimentally proven to inhibit CYP2C9 were not considered here. Given the performances of the best RF and SVM models with 36 MOE + 7 IE descriptors, we decided to combine both methods to identify unknown drug inhibitors of CYP2C9. The MOE descriptors and the interaction energies for the seven CYP2C9 protein conformations were calculated for the 4480 drugs. The consensus of the best RF and SVM models resulted in 2139 common drugs predicted as inhibitors of CYP2C9. To identify the most potent CYP2C9 drug inhibitors, we applied an additional filter of IE < -8.
CYPs are susceptible to be inhibited by a large variety of compounds, and thus, the high number of drugs predicted here to inhibit CYP2C9 seems to be reasonable. To select diverse drugs for the in vitro validation, we performed chemical diversity clustering with MOE software using MACCS fingerprints and a Tanimoto similarity cutoff of 0. 80. The resulted 109 drug centroids were additionally classified using DataWarrior software  [49]  with the FragFp structure descriptor and a similarity cutoff of 0. 70. Twenty-four clusters were thus obtained, as two of them combined diverse drugs with a structural FragFp similarity below 0. 50 (Cluster 3 contained 18 molecules, and Cluster 24 contained 37 molecules).
In vitro assays of CYP2C9 enzyme inhibition The inhibition assays were performed with 18 candidate molecules (Table  5 ) selected from the 24 diversity classes after in silico screening. To obtain a representative selection of potential inhibitors and substrates of CYP2C9 enzyme we took one drug per cluster when available from commercially available libraries and for the largest clusters 3 and 24 we selected two and three diverse drugs, respectively. To the best of our knowledge, these 18 compounds have not been reported in the literature or in publicly available databases as experimentally validated inhibitors of CYP2C9. Their molecular structures are shown in S6 Table. Inhibition assays were first performed using HepG2 cells expressing high activity for the CYP2C9 enzyme. The 18 studied molecules were tested at a range of concentrations to assess the dose-dependent inhibition effect reflecting competitive inhibition mechanism towards the catalytic site of CYP2C9.
https://doi. org/10. 1371/journal. pcbi. 1009820. t005 inhibition of CYP2C9 in supersomes, similarly to the results obtained with HepG2 cells. These results showed that the two approaches (HepG2 cell lines expressing CYP2C9 and CYP2C9 supersomes) resulted in similar interpretations for the studied compounds. Our inhibition results showed that relatively "small" or "big" drugs can inhibit the CYP2C9 activity. The large binding pocket of CYP2C9 is composed of two smaller cavities: the so called "warfarin-binding site" (PDB ID 1OG5)  [50]  and the catalytic site cavity close to the heme cofactor. Interestingly, losartan can bind simultaneously in both cavities as observed in the crystal structure of CYP2C9 with co-crystallized losartan (PDB ID 5XXI). The predicted binding positions of the drugs vatalanib and ticagrelor, which have different sizes and strongly inhibit CYP2C9, are shown in Fig 4.
Conclusion We developed and validated a new ML approach for the prediction of CYP2C9 inhibition. We built predictive models by combining an original selection of physicochemical descriptors of CYP2C9 inhibitors, CYP2C9 protein structure and dynamics knowledge, and machine learning SVM and RF modeling. The validation on PubChem and ChEMBL data demonstrated that our models successfully predicted CYP2C9 inhibitors with an accuracy, sensitivity and specificity of approximately 80%. The application of this approach allowed to propose 18 drugs for in vitro validation of CYP2C9 inhibition. We provide here the first identification of the drugs vatalanib, piriqualone, ticagrelor and cloperidone as strong inhibitors of CYP2C9 with IC values <18 μM and sertindole, asapiprant, duvelisib and dasatinib as moderate inhibitors with IC50 values between 40 and 85 μM.
Training and external test data sets preparation We used the PubChem BioAssay datasets of CYP2C9 AID 883 and 1851 containing data of CYP2C9 inhibition for 27463 chemical compounds. In addition, we collected 5114 compounds from ChEMBL tested for CYP2C9 (the ChEMBL IDs are given in S1 Text). We retained the 8851 most active inhibitors from PubChem and ChEMBL data with AC50 (or IC50) values � 10 μM (AC50, "activity concentration 50" refers to the concentration that is required to elicit the half-maximal effect), and 6056 non-inhibitors from PubChem showing less than 10% inhibition at a concentration of 50 μM. This collection was filtered for duplicates using the web server FAF-Drugs4  [33]  and an in-house-developed "soft" drug-like filter for physicochemical properties (see S1 Text) without removing toxic/reactive/PAINS (Pan Assay Interference) compounds since toxic or PAINS compounds could also be CYP inhibitors.
The 3D compound structures were generated using the freely available web server Frog2  [57] , and the compounds were protonated at pH 7. 4 using the major macrospecies option of the Che-mAxon calculator plugins (www. chemaxon. com). Gasteiger atom charges were added using the AutoDockTools package  [58]. Structural clustering was performed using OSIRIS Data-Warrior software  [49]  with the FragFp Structure descriptor and a similarity cutoff of 0. 85. Only the cluster centroids were taken resulting in the final filtered and diverse dataset containing 4840 inhibitors and 3301 non-inhibitors. The training set was built by randomly chosen 80% of both active and inactive compounds of the final dataset. The remaining 20% of the molecules were used as external test set.
Ensemble docking Two X-ray CYP2C9 structures were obtained from the Protein Data Bank (PDB): 5XXI cocrystallized with losartan  [34]  and 1R9O  [35]  co-crystallized with flurbiprofen. The MD structures were generated from previously performed MD simulations  [30]. The pKa values of the titratable groups of CYP2C9 were calculated with the FDPB approach using the PCE web server  [59]. The charges were assigned using the AutoDockTools package  [58]. The heme was kept in the intermediate Cpd I state  [60]  since our preliminary tests showed similar docking scores calculated using Cpd I or ferric heme. We performed virtual screening and docking of the dataset compounds using the well-established free software AutoDock Vina  [36] , which employs gradient-based conformational docking and an empirical scoring function predicting the protein-ligand binding energy in kcal/mol.
The grid resolution was set to 1 Å, the maximum number of output binding modes was fixed to 10, and the exhaustiveness level was set to 8. The grid included the whole binding pocket of cytochrome CYP2C9. The grid center coordinates used were 8. 208, 32. 219, -1. 923, and the size of the search space was set to 25 Å×25 Å×25 Å according to PDB ID 1R9O. The best docking score for each protein conformation was retained. The docking performance of Autodock Vina for CYP2C9 was checked by preliminary docking of flurbiprofen into the structure of CYP2C9 co-crystallized with flurbiprofen (PDB ID 1R9O).
Machine learning classification modeling Random forest. RF classification  [61]  was performed using the Random Forest R library  [62]  of the statistical software package R. Multiple decision trees were built with bootstrap samples from the training data. To introduce diversity between the trees of the RF, a small subset of descriptors was randomly selected to make decisions at each node of each tree. The classification was obtained by taking the results of all the trees through a majority vote. To find the optimal size of the forest (ntree is the number of trees) and the number of descriptors (mtry is the number of selected descriptors) for each model based on different numbers of descriptors, we ran RF calculations scanning over the ntree (25-500) and mtry (5-13) parameters (see SI for details).
The maximum value of mtry was set to 13 according to the widely accepted concept that the value of mtry should be equal to p p, where p is the total number of variables  [63]. For each model, we selected the combinations of ntree and mtry parameters that yield the best internal accuracy while retaining the lowest acceptable ntree (see S3 Table ). A ten-fold crossvalidation procedure that was repeated five times. Support vector machine. SVMs are based on the minimization principle from statistical learning theory and place data into a hyperspace through a kernel function for its separation into datasets for classification or regression modeling  [64]. For the nonlinearly separable cases, PLOS COMPUTATIONAL BIOLOGY the kernel function allows SVM to transfer the data points into a higher-dimensional space where linear separation is possible.
The plate was finally incubated at room temperature in the dark for 30 min to stabilize the luminescence signal, which was measured using an EnSpire plate reader (Perkin Elmer). Kinetics of inhibition tests with HepG2 cells expressing CYP2C9. Concentrationdependent CYP2C9 inhibition assays were performed by preparing a five-step dilution series from 0. 5 (min) to 50 μM (max) of selected compounds according to the viability tests and by transferring 12. 5 μl of each dilution into the assay plate. CYP2C9 activity was determined (as described above) and compared to that of untreated controls incubated with DMSO buffer alone. Each compound dilution was tested in triplicate in the inhibition assays, and the experiments were independently repeated on three different days. The data were analyzed with GraphPad Prism Version 5.
Acknowledgements This work was financially supported by the  Center for Human Systems Immunology with the Bill & Melinda Gates Foundation  ( OPP1113682 ;  OPP1211043 ;  INV027411 ), the  American Cancer Society  ( RSG-18-133-01-CDD ), the  Goldman Sachs Foundation  (administered by the  Stanford Cancer Institute , SPO#  162509 ), and a  Bio-X Interdisciplinary Initiatives Seed Grant. B. S. O. is grateful for a  Eastman Kodak Fellowship. O. M. S. is thankful for a National Science Foundation Graduate Research Fellowship. J. B. is thankful for a  Marie-Curie fellowship  from the  European Union  ( H2020 ; No. 101030481 ).
Each unique triplet association [genotype-drug-ADR] may be studied across different published meta-analyses. We gathered all the informative clinical studies assessing the same triplet association [genotype-drug-ADR] reported across the published metaanalyses from the different systematic reviews. Combining the data from previous overlapping published meta-analyses allowed increasing sample size of data given the number of clinical studies. We then assessed the presence of a publication bias and its impact for each unique triplet association [genotype-drug-ADR].
Information sources We conducted an umbrella review on PubMed, up to January 29, 2019, seeking published meta-analyses investigating the association between a genetic variant and an adverse reaction in patients treated with antiseizure drugs. We limited the information source to the Medline database. Indeed, the Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for HLA genotype and Use of Carbamazepine and Oxcarbazepine was restricted to the use of "the PubMed database"  [11]. Using the same restriction allowed to estimate the impact of publication bias in the context of the current practice of guidelines elaboration in this area.
Search strategy The Medline database was searched using the following keywords: "pharmacogen � " OR "genetic variant" OR "polymorphism" OR "allele" OR "association" AND "epilepsy" OR "antiepileptics" OR "anticonvulsants" AND "adverse event" OR "toxicity" OR "serious event" OR "meta-analysis" OR "systematic review" (see the search strategy in Supplementary information), without restrictions regarding the year of publication.
Study selection and data extraction Abstracts of bibliographic references were screened based on their title and abstract, and then selected using their full text, reason for exclusion being tracked. We extracted descriptive characteristics: authors, publication date, type of study, and ethnicity. We extracted study design, pharmacogenetic associations as reported by the authors (name of the genetic variant, antiseizure drug assessed, control treatment used, and adverse reactions). Indeed, as we used previously published systematic reviews of clinical studies, our extraction is limited by the author's definition of the genetic variant (i. e not the rs number if not reported) and of the adverse drug reactions. We extracted the odds ratios (OR) with their 95% confidence intervals (95%CI) of each clinical studiy reported in the included published meta-analyses.
Synthesis of result-data analysis For each unique triplet association [genotype-drug-ADR] , we gathered the ORs of the clinical studies from different published meta-analyses. We limited the compilation to comparisons using treated patients as control groups. We used the funnel plot approach for assessing potential publication bias  [22, 23]. We generated the funnel plots for each included associations, for estimating the prevalence of publication bias. Firstly, we conducted a visual analysis of the generated funnel plots  [24]. Two researcher independently assessed if a publication bias was 'likely', 'unlikely', or 'not determinable' (SB, GG). Agreement was estimated using a Free-marginal kappa estimator  [25]. A third researcher helped resolve disagreements, blinded to the previous diagnoses (JC).
Implications of the study Our study showed that,-as in clinical pharmacology  [3]  or in genetics  [4] -, publication is selective in pharmacogenetics. We showed that this publication bias may affect the assessment of an association between genetic biomarker and ADR, even for consensual pharmacogenetic biomarkers of antiseizure drug's AE as HLA-B � 15:02. Our study showed the need for publishing or at least registering any pharmacogenetics study, even with inconclusive results, to fight the issue of publication bias and its harmful consequences. The increased access to genomic databases and to genomic data through next generation sequencing strengthens the importance to anticipate such issues.
In this study, the effect of temperature during the evaporation of a drop of sheep's blood on the dynamics of evaporation was analyzed by measuring the variation in droplet volume and height as a function of time, and by calculating the number of thermal Marangoni waves between the center and the drop contact line as a function of time. The different induced patterns could be transformed into 3D parameters by studying the topography of their surfaces. This type of analysis is used to easily find the differences between the different induced patterns and characterize each pattern. These parameters are included in the ISO 25178 and EUR 15178N standards  [21] , specially developed for analysis at the microscale. The surface topography datasets were well characterized by ISO 25178, providing an important reference for the definition of standard procedures and parameters.
Slope of the volume shrinkage curve From the aforementioned results, it can be observed that the volume of the drop decreased linearly at the beginning. For this, the slopes of these curves were determined and studied first as a function of the temperature and then as a function of the temperature and volume of the drop (see Appendix Figure  21  and Figure  22 ). Fifty-eight tests were performed in this study, for which all data were available. The slope increased with the surface temperature. This slope is related to the evaporation rate; the higher the slope, the higher is the evaporation rate. The values of the regression coefficient were acceptable; therefore, this parameter was used to characterize sheep blood in the remainder of our study.
Statistic study In this study, data from sheep blood samples (72 tests, 18 tests for each temperature) were used. One-way analysis of variance (ANOVA) was performed. The Tukey-Kramer method was used to compare the values of the parameters at different temperatures (groups). This function provides pairwise comparison results of a multiple comparison test using information from the groups. To determine the error of the ANOVA and Tukey-Kramer statistical tests, a few studies were investigated by changing the distribution index, number of treatments, and sample size via simulation. The exponential distribution using a small number of treatments yielded the largest differences with a deviation of approximately 0. 3%, and this value decreased by increasing the number of treatments and the index for the gamma distribution  [32].
Results and Discussion To screen for new anti-infective natural products, we fermented numerous myxobacteria in liquid media in shake flasks, independently, and analyzed their production profile using uHPLC-hrMS. One strain, C. coralloides ST201330 produced a compound with a m/z [M + H] + value of 1184. 56, which could not be matched to known natural products in SciFinder and DNP database searches. Thus, we purified this natural product that was named Corramycin (1) from the fermentation broth of ST201330 shake flask cultures. Notably, we also identified another strain, C. coralloides MCy10984, producing the same compound. Detailed analysis of the 1D and 2D NMR spectra revealed the twodimensional structure of Corramycin (Figure  1 ; Supporting Information section 1). Corramycin exhibits a linear peptidic structure with eight α-amino acids including several unique structural features such as the unprecedented γ-N-methyl-β-hydroxy-histidine, a 5-amino-2,3-dihydroxy-4-methoxy valeroyl moiety and an N-terminal dihydroxy butyric acid moiety.
To fully elucidate the stereochemistry of Corramycin (1), we performed a total hydrolysis followed by chiral GC/MS analysis of the obtained amino acids. The hydrolysis revealed the absolute config- uration of six stereocenters (labelled in blue in Figure  3 ). The relative configuration of the two vic-dihydroxy units was elucidated via acetonide synthesis and ROESY NMR experiments (acetonide 6, Supporting Information Figures S18-S20). Based on this analysis, we started to synthesize all possible remaining 16 diastereomers of Corramycin to compare the respective NMR data with those of the isolated natural product. A solid-phase peptide synthesis (SPPS) approach with a traditional Fmoc-strategy and adequate protecting groups on the different hydroxyl groups was used to synthesize Corramycin starting from L-serine.
5, b2. 7, c2. 4, d1. 3, d1. 4, e1. 4, f2. 1, f2. 5 (see Fig. 11A  for the resulting dataset). The mixed-effects model considers "Runs" as the random effect, i. e. a correlated slope and intercept with the formula potentially. absorbed ~ Lab + (Lab |Run). The obvious weakness of the model is that the random factor has only two levels and is strongly correlated. However, the model's residuals appear to be normal distributed when investigated by graphical analysis (plot not shown). The analysis is presented in Fig. 11B. It shows that the results of all laboratories are comparable to the grand mean of 3. 37%, when runs are considered as a random effect. Further, the laboratories have compatible means when compared to each other with a Tukey test  (Tukey, 1949)  on the same mixed-effect model (data not shown).
, 2014; Sullivan et al. , 2017; Hopf et al. , 2020). Several laboratories have reported individual study results with the reference compounds mentioned in the OECD test guideline. Their intend was usually to assess particular aspects of the methodology or assessing the impact of vehicle components and thus not necessarily aiming to apply an across laboratories routinely standardized methodology  (Abd et al. , 2019; Dreher et al. , 2002; Guth et al. , 2015; Hewitt et al. , 2020; Heylings et al. , 2018; Hui and Maibach, 2020; Im et al. , 2021; Wilkinson et al. , 2006). Therefore, it is not surprising that reported caffeine absorption values in % of the applied dose vary generally in the literature. Even the recently reported caffeine testing in the cosmetic component database  (Hewitt et al.
Conclusion The ring trial demonstrates that the OECD TG 428 assay can be robustly and reliably performed by different laboratories. However, the experiment indicates that other skin associated factors than internal integrity testing may affect absorption and variation. Skin quality criteria not routinely assessed or considered in the current method may explain dermal absorption variations observed in practice regulatory testing, i. e. , outliers within a dataset or differences between datasets. Hence, it is suggested to explore further quality control measures for the used skin samples. Rajendra Nagane, Robert Mignoia, Christine Whitfield, John Kendrick, Clair Valentine, Jeanne Bernal Dorange, Camile Egron and Camile Imart are study directors and scientists from the laboratories that conducted the studies.
Acknowledgements The authors would like to thank the participating laboratories of the 1st ring trial,  BASF SE Biokinetic Laboratory of Experimental Toxicology and Ecology, Charles River Laboratories Den Bosch and Charles River Laboratories Edinburgh, Labcorp Drug Development, CXR Biosciences, Dermal Technology Laboratory, Envigo CRS, Eurofins Agroscience Services Chem SAS, Innovative Environmental Services ,  Jai Research Foundation ,  Pharmaron UK  and  Triskelion B. V. Felix M. Kluxen ,  Jeanne Domoradzki ,  Philip Fisher ,  Christine Lorez ,  Edgars Felkers ,  Bob Parr-Dobrzanski ,  Steven McEuen  and  Christiane Wiemann  (chair) are members of the CLE Dermal absorption technical subgroup. Wilfred Maas ,  Frank Toner ,  Leanne Page ,  Kathryn Webbley ,.
Introduction: Because patients and patient organizations want to strengthen their role in the care pathway and drug evaluation and in order to improve pharmacovigilance activities, European competent authorities implemented regulations to allow direct reporting of adverse drug reactions related to medicinal products by patients in 2012. Objectives: Describe and study determinants and evolution of patient reporting activity in France to assess patient involvement in pharmacovigilance. Method: Using the French national pharmacovigilance database, univariate and multivariate analyses were performed to compare characteristics of patients and healthcare professionals (HCP) adverse drug reaction (ADR) report from 2011 to 2020. Then, we analyzed ADR report activities from patients according to regional demographics and economics characteristics.
Then, these adverse reactions reports are evaluated and recorded into the French Pharmacovigilance database (BNPV) by the CRPV since 1985. This anonymized information is transmitted to the French drug agency (ANSM: Agence Nationale de Sécurité du Médicament et des produits de santé) which coordinates the whole process  (4). Since 2011 and 2012 respectively, French and EU pharmacovigilance legislations require all countries across the EU to allow reports directly from patients  (1, 5). In 2017, the French Ministry of Health set up a reporting web portal to enhance vigilance and simplify reporting (6). Adverse events, whether they are linked to health products, products of everyday life, psychoactive substances or occurred during a care act, can now be reported, through a simple and unique tool.