dataesr/scientific-paragraphs-categorization huggingface dataset:
All performance metrics are displayed in each plot. The subset with the most reliable ComboScore RF predictions (top left plot) achieves half the RMSE of the entire test set (bottom right). Importantly, this is a much larger reduction in RMSE than that introduced by XGB (bottom right) with respect to RF (bottom left). Furthermore, the most reliable predicted ComboScores (top right) obtain a third of the RMSE of the least reliable predictions (top left). cross-validation experiments for each cell line. In LODO crossvalidation, every combination containing the considered leftout drug is placed in its test set, and the model is built on the remaining combinations tested on that cell line. Thus, there are as many folds as drugs in the dataset. In this way, the LODO crossvalidation simulates the model's behavior when presented with a new chemical entity outside of the model's scope, as if it was not included in the dataset.
28). Conversely, partners of tyrosine kinase inhibitors, well-represented in these datasets (e. g. , Imatinib, Nilotinib or Although there are no large differences in how well different cancer types are predicted, left-out drugs on melanoma (ME, in red) and leukemia (LE, in green) cell lines obtain slightly higher average performance (median R p of drug-out models for corresponding cell lines are 0. 554 and 0. 524, respectively). Lapatinib), are predicted with high accuracy (e. g. , models for imatinib have median R p = 0. 82). Topoisomerase inhibitors (Teniposide and Etoposide) are also among the best-predicted left-out drugs. These in silico models could be used to anticipate how the synergies of a drug in combination with its partner drugs would vary across NCI-60 cell lines. However, since high accuracy is only obtained on those left-out drugs wellrepresented in NCI-ALMANAC, such selectivity predictions should only be accurate for drugs with similar chemical structure to those in NCI-ALMANAC.
Comparing Predictive Models Built With Data From Different Screening Centers So far we have exclusively employed data from the FG screening center, which represents about half of NCI-ALMANAC data. Practically all the remaining ComboScores come from the FF screening center and are also determined with a 3 × 3 grid of FIGURE 8 | RF model performance comparison for FG and FF datasets. Models are built following the final setup in the exploratory analysis (a 90/10 data partition is employed for each cell line); MFPC with physico-chemical features as well as data augmentation are also used). On the left, boxplots for cell line models test set R p 's (top row) and RMSE (bottom row) for both centers data. On the right, R p (top row) and RMSE (bottom row) of models trained on FG dataset against models trained on FF dataset, each point shows the two model performances for the cell line.
FF models obtain consistently lower performance than FG models. As the same modeling workflow was used, this strongly suggests that FF data is less predictive than FG data. non-zero concentrations. Thus, we evaluate here the predictive potential of FF datasets. We start by building RF models from FF data using the same 90/10 partitions as with FG. Surprisingly, FF-based models obtained worse performance in every cell line (Figure  8 ) and thus were objectively worse at predicting ComboScores. In trying to understand this unexpected result, we started by investigating whether this was due to modeling differences, but this was not the case. First, FF training sets are slightly larger than FG datasets (see Supplementary Table  1 ), which theoretically favors better performance on FF. Furthermore, using tuned XGB models led to essentially the same result (median R p of 0.
We first consider the drugs on a cell line were only FG models obtain high average accuracy in predicting synergy (NCI/ADR-RES) and subsequently on another where both FF and FG models are on average predictive (NCI-H322M). On each FIGURE 9 | RF model performance comparison for FG and FF datasets. Models are built following the final setup in the exploratory analysis (i. e. , a 90/10 data partition is employed for each cell line; MFPC with physico-chemical features as well as data augmentation are also used). We analyzed five drugs (Thioguanine, Chlorambucil, Altretamine, Fluorouracil, and Melphalan) with a high number of different test dates in both centers. On the left, the results with the cell line that is worst predicted by RF with FF data (NCI/ADR-RES with R p = 0. 14 in 90/10 partition), which is much better predicted with FG (R p = 0.
65, using the same partition). This plot shows the standard deviation of the values of each set from FG against those from FF. cell line, each drug has a set of growth inhibition replicates per concentration and screening center (i. e. , 15 sets per screening center). The performance on NCI/ADR-RES using FF data is indeed poor (R p = 0. 14 in 90/10 partition by RF), but it is much better predicted using FG data (R p = 0. 65, using the same partition and method). Fourteen of the fifteen sets have higher standard deviation of growth inhibition with FF data (Figure  9 ), which is consistent with the lower accuracy in predicting synergy obtained with this dataset. Conversely, we repeated this operation with NCI-H322M where synergy is well-predicted by RF with both FF (R p = 0. 61 in 90/10 partition) and FG data (R p = 0.
DISCUSSION NCI-ALMANAC is an extremely valuable resource for the discovery of novel synergistic drug combinations on NCI-60 cell lines. First, it is by far the largest-to-date HTS of drug combinations, therefore allowing in silico models with much higher accuracy and broader domain of applicability in predicting the synergy of other combinations. Second, some of the synergistic drug combinations discovered in vitro by NCI-ALMANAC were subsequently tested on human tumor mouse xenografts of the same cell line. 48% of them were also synergistic in at least one of these in vivo models  (Holbeck et al. , 2017) , which led to the launch of two clinical trials so far (NCT02211755 and NCT02379416). In this study, we have found that it is possible to predict the synergy of unseen drug combinations against NCI-60 panel cell lines with high accuracy by exploiting NCI-ALMANAC data.
We have established a general ML workflow (types of structural features, data preprocessing strategy, ML method) to generate such models. When trained on FG data, predicted synergies from these models match observable synergies with R p correlations comprised between 0. 43 and 0. 86 depending on the considered cell line. Incidentally, these regression problems must be highly non-linear, as EN leads to substantially less predictive models than XGB or RF. Some cell lines and drug combinations can be predicted with higher accuracy than others. For example, models for the SK-MEL-5 cell line perform best with any method (Figure  6 ). However, if we use RMSE instead of R p to reduce the influence of the ComboScore range, models for the NCI-ARD-RES are now best (gray squares in Figure  7 ). Another explanatory factor for this variability is the adequacy of the employed ML technique to the problem instance to solve (each cell line constitutes here a different problem instance).
Even if training set size, features and classifier are the same, the modeled relationship between drug synergy and features depend on training set composition and cell line properties (implicitly). It is well-established that the performance of supervised learning algorithms varies depending on the problem instance in ways that cannot be anticipated without doing the actual numerical experiments  (Fernández-Delgado et al. , 2014). LODO cross-validation also revealed both best and worst partner drugs. These differences are mainly due to the number of similar partner drugs. For example, it is difficult to predict synergy of combinations containing arsenic trioxide because its 103 partner drugs are highly dissimilar in terms of chemical structure and physico-chemical properties. Indeed, machine learning from dissimilar data instances tend to be less accurate, although here the dissimilarity can be partial as arsenic trioxide's partner can be similar to other NCI-ALMANAC drugs.
Lastly, highly synergistic combinations predicted with low reliability should also be tested, as the corresponding measurements would be those broadening the applicability domain of future models the most. We have also found that using FG datasets leads to substantially more predictive models than FF datasets. This result is robust in that it is observed with various types of models (XGB, RF, EN). Moreover, it occurs in spite of the availability of slightly more training data. Further investigation revealed that there are many more measurements of growth inhibition and with greater variability in FF than in FG. This in turn introduces more noise into ComboScore determinations in FF, thus impairing its modeling. Inconsistencies between centers measuring the response of cancer cell lines to drugs have been observed before  (Haibe-Kains et al.
, 2013). There has been intense controversy about the extent, sources and impact of these inconsistencies  (Stransky et al. , 2015; Geeleher et al. , 2016; Safikhani et al. , 2016 Safikhani et al. , , 2017)). In any case, it is clear that data permits the development of predictive models regardless of the screening center  (Ammad-ud-din et al. , 2014; Covell, 2015; Fang et al. , 2015; Naulaerts et al. , 2017) , as it has also been the case here with NCI-ALMANAC. Owing to this controversy on datasets from multiple screening centers, a better understanding of their limitations and the identification of protocols to generate them with improved consistency has emerged  (Haverty et al. , 2016). These protocols will ultimately permit that merging datasets from different screening centers result in further predictive accuracy.
Transmission electron microscopy (TEM) Morphology of NC-PA was analyzed by TEM images. 5 µl of nanoconjugate was deposited onto copper grids covered with a formvar film (400 mesh) for 2 min. Then, NC-PA were covered with 5 µl of a 0. 5% (w/v) uranyl acetate solution, for 30 s. Colorant excess was removed with a filter paper and samples were observed in a JEOL JEM-1400 microscope operating at 80 kV with a filament current of about 55 µA, a post-column high-resolution (11 megapixels) and a high-speed camera (SC1000 Orius; Gatan). Data were acquired from Digital Micrograph (Gatan).
HPLC system HPLC was employed to quantify PA amounts. The system was composed of a Waters 1525 Binary HPLC Pump, an injector Waters 2707 Autosampler and an UV-visible Waters 2998 Photodiode Array Detector. Data acquisition was done using the Empower software. PA was assessed following the protocol described by Simões et al  [11]. An RP18 Hydro (250 mm x 4. 6 mm x 5. 0 µm, Phenomenex) column was used at a temperature of 35 ºC. The eluent was composed of a mixture of acetonitrile and 25 mM phosphate buffer (pH 2. 0 adjusted with phosphoric acid) in a proportion of 5:95% (v/v). The isocratic flow rate was 1. 0 ml/min and UV detection was set at λ = 267 nm. PA retention time was 5. 47 min. Values reported on each time point analysis were plotted on a PA calibration curve (5 -200 µg/ml) and quantified following the equation y = 65936x -92640 (R² = 0.
Statistical analysis data are presented as mean values with standard deviation (SD). Statistical analysis was performed with the Prism Graph-Pad 7. 0 software and the significance was calculated using a one-way ANOVA method, in which p < 0. 05 was considered statistically significant (*p < 0. 05, **p < 0. 005, *** p < 0. 0005, **** p < 0. 0001).
Acknowledgements Authors would like to thank  Stéphanie Denis  ( Institut Galien Paris-Sud ) for her help with cell culture and  Valérie Nicolas  from  MIPSIT  for helping with confocal microscopy. The present work has benefited from the core facilities of  Imagerie-Gif , (http://www. i2bc. paris-saclay. fr),member of IBiSA (http://www. ibisa. net),supported by " France-BioImaging " ( ANR-10-INBS-04-01 ), and the  Labex "Saclay Plant Science"  ( ANR-11-IDEX-0003-02 ).
Acknowledgements The authors thank the financial support from  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes, Brazil)  and  Institut Galien Paris-Sud. Thanks to  Anne Beilvert  and  Emeline Servais  for their chemical help with the synthesis procedure,  Audrey Solgadi  for mass spectrometry analysis, BioCis platform for 1 H NMR analysis. The present work has benefited from the core facilities of  Imagerie-Gif , (http://www. i2bc. paris-saclay. fr),.
Search strategy and selection criteria MEDLINE, Web of Science, Embase, and Cochrane Data of Systematic Reviews were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (S1 Checklist). As previously described  [9, 16] , prospective studies evaluating the efficacy of antimalarials against uncomplicated P. vivax that were published between January 1, 2000, and January 31, 2018, in any language were identified (S1 Text). Two independent investigators undertook the review and extracted the data (RJC and RNP), resolving discrepancy through discussion. Principal investigators of eligible studies were invited to share individual patient data and any additional data from eligible unpublished studies. Studies were included if they assessed treatment with DP or AL with or without PQ.
Procedures Individual patient data were excluded if baseline parasitaemia involved mixed species or blood schizontocidal treatment was incomplete. Early PQ was defined as commencement of PQ within the first 3 days of ACT treatment. Drug doses were calculated from the number of tablets a patient was administered if data were available or were based on the study protocol, assuming complete adherence. ACT partner drugs were considered determinants for recurrence rather than artemisinins, which have short half-lives and are considered determinants for parasite clearance. Study sites were classified as having short (median time to patent relapse of 47 days or less) or long P. vivax relapse periodicity based on estimates from the Malaria Atlas Program for geographical location  [12]. PQ dose was defined as a low dose if the total dose was <5.
Statistical analysis Analyses were undertaken according to an a priori statistical analysis plan  [22] , unless stated otherwise, using Stata (version 15. 0) and R (version 3. 4. 0). The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO: CRD42016053310) prior to undertaking the analysis. To investigate inclusion bias, baseline characteristics of targeted studies not included in the analysis were compared with the characteristics of the studies included in the analysis. Study quality and risk of bias were assessed according to criteria for individual patient data metaanalyses  [23]. Kaplan-Meier survival analyses were used to calculate risk of recurrence according to the treatment administered. Patients were censored at the first day of any of the following for an outcome prior to day of the end point: recurrent parasitaemia, the last clinic visit, the last visit prior to a gap of >18 days between parasite microscopy results, the day of outcome, or the day of PQ treatment (if �28 days after enrolment).
Results Of 180 prospective P. vivax studies published between January 1, 2000, and January 31, 2018, 26 studies (14. 4%) with patients treated with DP or AL were eligible for inclusion. Individual patient data were available from 18 studies enrolling 4,946 patients, of which 3,640 were infected with P. vivax (67. 7% of the 5,377 targeted patients with P. vivax). Additional data were available for 45 patients enrolled in these published studies, but not formally reported, and 736 patients from two unpublished studies. Of 4,421 P. vivax patients with data available from eligible studies, 2,217 (50. 1%) were not treated with DP or AL, and an additional 187 (4. 2%) were excluded for other reasons, resulting in 2,017 patient records included in the analysis (Fig 1 and S1-S4 Tables). Of those patients, the median age was 18.
Acknowledgments We would like to thank all patients and staff who participated in these clinical trials at all the sites, the WWARN team for technical and administrative support, and the  Malaria Atlas Project  for transmission estimates. The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the  Centers for Disease Control and Prevention. Karunajeewa ,  Moses Laman ,  Kartini Lidia ,  Brioni R. Moore ,  Ivo Mueller ,  Francois Nosten ,  Ayodhia P. Pasaribu ,  Dhelio B. Pereira ,  Aung P. Phyo ,  Jeanne R. Poespoprodjo ,  Carol H. Sibley ,  Kasia Stepniewska ,  Inge Sutanto ,  Guy Thwaites ,  Tran T. Hien ,  Nicholas J. White ,  Timothy William ,  Charles J. Woodrow ,  Philippe J. Guerin ,  Ric N. Price.
The WWARN is registered with the Registry of Research Data Repositories (RJC is supported by a  Postgraduate Australian National Health and Medical Research Council Scholarship (NHMRC)  and a  RACP NHMRC Kincaid-Smith Scholarship. RNP is a  Wellcome Trust Senior Fellow in Clinical Science  (200909). JAS is funded by an  Australian   NHMRC Senior Research Fellowship   1104975 ,  NMA  by an  NHMRC Senior Principal Research Fellowship   1135820 , and MJG by an  NHMRC   Early Career Fellowship   1138860. KT is funded by the  Asia Pacific Malaria Elimination Network (APMEN)  and OPRA clinical trial funding, supported by the  Bill & Melinda Gates Foundation  ( OPP1164105  and  OPP1054404 ). WWARN is funded by  Bill and Melinda Gates Foundation and Exxon Mobil Foundation  grants. This work was supported by the  Australian Centre for Research Excellence on Malaria Elimination (ACREME) , funded by the  National Health and Medical Research Council of Australia  ( 1134989 ).
The screening was done on the basis of the pharmacophore hypothesis (Figure  20b ) developed from interactions of TrkB and its synthetic ligand-LM22A-4 (Massa, S. M. , et al. J. Clin. Invest. 2010, 120, 1774-1785). On the basis of the main structural features of the LM22A-4, we designed a small dataset of the new potential TrkB agonists and conducted the synthesis of the compounds with best in silico results. After conducting in silico studies on the crystal structure of the TrkB receptor in order to determine the most important interactions between TrkB and its natural ligand-neurotrophin-4/5 (NT-4/5) (Figure  20a )-we performed in silico screening of the unique CERMN (Centre d'Etudes et de Recherche sur le Médicament de Normandie) library of compounds. The screening was done on the basis of the pharmacophore hypothesis (Figure  20b ) developed from interactions of TrkB and its synthetic ligand-LM22A-4 (Massa, S.
M. , et al. J. Clin. Invest. 2010, 120, 1774-1785). On the basis of the main structural features of the LM22A-4, we designed a small dataset of the new potential TrkB agonists and conducted the synthesis of the compounds with best in silico results. This project received funding from the European Union′s Horizon 2020 framework programme for research and innovation under grant agreement no. 765704.
The importance for the antiplasmodial activity of the phenyl ring contained in the bicyclic quinazoline and quinoxaline scaffolds was also studied by structural simplification, leading to the related pyrimidine and pyrazine analogues. The synthesis details of the new analogues of hit 1 in various series and the biological results will be described in the poster. This work was supported by the ANR NINTARMAL project, grant ANR-17-CE11-0017. 7. 29. Optimisation of Peptide Linker-Based Fluorescent Ligands for Histamine H1 Receptor (P29) Zhi Yuan Kok 1  , Shailesh N. Mistry 1 , Stephen J. Hill 2 and Barrie Kellam 1 The histamine H 1 receptor (H 1 R) is a class A GPCR and is widely expressed throughout the human body. H 1 R-dependent signalling is responsible for allergic reactions, plays an important role in immunomodulation and, as recent studies have suggested, is involved in mediating cell proliferation and thus cancer progression (Panula, P.
Acknowledgements: Tenovus Scotland, The Engineering and Physical Sciences Research Council (EPSRC) 7. 11. Stories from Staudinger: Synthesis of Chiral Beta-Lactams (P11) Niamh O Boyle, Mary Meegan and Eavan McLoughlin 1 School of Pharmacy and Pharmaceutical Sciences, Trinity College, University of Dublin, Dublin, Ireland; mclougea@tcd. ie Combretastatin A-4 (CA-4) is the most potent antimitotic agent of the combretastatin A series, a group of diaryl stilbenes isolated from the wood of the South African tree Combretum caffrum (Watt, J. M. , et al. The Medicinal and Poisonous Plants of Southern and Eastern Africa. E. & S. Livingstone Ltd. : Edinburgh and London, United Kingdom, 1962). It has significant anticancer activity through inhibition of tubulin polymerization and microtubule assembly (Pettit, G.
Acknowledgments SD would like to acknowledge the  China Scholarship Council  for his Ph. D fellowship. CS is grateful to  P. Allgayer  for the mechanical engineering support. Both electronic microscopy platforms of the  Institut de Génétique et de Biologie Moléculaire et Cellulaire  and the  Institut Charle Sadron  are acknowledged for the TEM micrographs. The company Fujifilm is also acknowledged for providing the KM micromixer and  Prof. Kazuhiro Mae  for helpful discussion.
It allows identifying the different determinants and quantifying their role. The major difficulty involves several parasitic factors that can influence the measurement and comparison  [12]. To avoid what is called confounding bias, clinical pharmacologists uses matching in clinical trials (in subjects' selection). In contrast, pharmacoepidemiologists perform adjustments (in the statistical analysis). In this analytical approach, one of the first methods commonly used, especially for the study of ADRs in a pharmacovigilance database, is the case / non-case method, which investigates disproportionality between the number of ADRs registered with the drug of interest and the average number of ADRs recorded with all other drugs  [13, 14]. It is thus possible to validate a safety signal which will be later confirmed using a quantitative and confirmatory approach, like case-control or cohort studies.
This method also allowed to strength the link between venous thromboembolism and exposure to fibrates  [24]. Of course, pharmacoepidemiology also uses the conventional methods of analytical epidemiology, including retrospective (case-control) or prospective studies (such as cohort surveillance). In pharmacoepidemiology, a case-control study allows to measure the association between a drug and a (desirable or not) event undetectable during clinical trials (for the reasons described above). Since case-control studies first include cases for the analysis, they especially allow detecting rare or delayed onset events. In contrast, cohorts are the method of choice for studying a high frequency drug event. Other methods  [25] [26] [27]  are also used: meta-analyses, database cross-referencing, case-population studies.
If the use of several of these methods to answer a question generally helps to strengthen the association studied, it may happen that discordant results are observed between different studies using different methods. This is the case, for example, for pancreatitis with incretinomimetic hypoglycemic drugs: the association, highly significant during the non-case case approach  [28] , was not found in a cohort study  [29]. This allow to stress the absolute need to use multiple (and therefore complementary) data sources and approaches in pharmacoepidemiology. The difficulties of pharmacoepidemiological studies in France are often stressed because of the presumed absence of drug databases. In fact, despite that, in contrast to northern European countries, the freely available French bases are essentially medico-administrative and therefore do not generally contain medical diagnoses, several pharmacoepidemiological studies of great scientific and medical interest are possible and were performed in France  [30] [31] [32].