dataesr/scientific-paragraphs-categorization huggingface dataset:
mapbayr estimation workflow The same workflow is followed within mapbayr (Figure  1 ). The feasibility of this transposition to R has previously been suggested. 29 Predictions (F ij ) are computed based on information available in the model code (parameter values, ODE) and data (dosing and covariates) using the ⎧ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎩ F ij = f ( ; x i ; i ) i = ( i1 , i2 , ⋯ , ik ) i ∼ MVN(0, Ω) Y ij = h(F ij ; ij ) ij ∼ MVN (0, Σ). OFV i = O � i � = -2LL � i � = � j ⎡ ⎢ ⎢ ⎣ log 2 ij + � Y ij -F ij � 2 2 ij ⎤ ⎥ ⎥ ⎦ + i T Ω -1 i , ̂ i = argmin O i F I G U R E 1 Workflow of a Shiny web app to perform therapeutic drug monitoring with maximum a posteriori Bayesian estimation performed by mapbayr. Data format, parameter estimation, and estimation report are common whatever the drug and can be assumed by mapbayr functions (in blue).
Computation of a specific a posteriori outcome and forecast of a dose adaptation is specific to the drug or protocol (in green). Arguments can be passed through a Shiny app (in red) so that the user enters information through a convenient interface. MAP, maximum a posteriori; PK, pharmacokinetics mrgsolve differential equation solver. The latter is a C++ implementation of LSODA, a solver for stiff and nonstiff differential equations, equivalent to the LSODA solver implemented in NONMEM in the subroutine ADVAN 13. 30  The OFV can be computed using observations (Y ij ) from the data set and Ω and Σ matrices defined in the model code. Finally, OFV is minimized by optimizing i using the limited-memory Broyden-Fletcher-Goldfarb-Shanno with box constraints (L-BFGS-B) algorithm implemented in the optimx package in R.
Analysis Parameters were estimated with mapbayr and NONMEM with software specifications mentioned in the Supplementary Materials. The performance of mapbayr was assessed by computing the maximum absolute difference on ̂ ik obtained with mapbayr (̂ ik,MBR ) and NONMEM (̂ ik,NM ) for every individual as follows: These values have no unit. However, provided that individual PK parameters ( i ) were log-normally distributed around a typical value ( TV ) such as i = TV • exp( i ), the maximum error on parameter value could be defined by Δ̂ ik = | | ̂ ik,MBR -̂ ik,NM | | , Δ̂ i = max Δ̂ ik , T A B L E 2 Δ ̂ i = i,MBR -i,NM i,NM = exp Δ̂ i -1. Thus, performance thresholds could be defined accordingly. If Δ̂ i was lower than 0. 001, estimation was considered excellent because the impact on parameter values would be negligible (Δ ̂ i < 0.
1%). If Δ̂ i was higher than 0. 095, the estimation was considered discordant (Δ ̂ i > 10%). In other cases, estimation was considered acceptable. The same classification was applied to the absolute error on the PK outcomes measured with the real models. Although NONMEM was considered as the reference that should return the best estimate of i , this assumption was challenged by comparing the OFV i returned by both software. However, the actual OFV i returned by NONMEM into the. phi file includes a constant term that is missing in the formula in the "MAP Bayesian estimation" Section. Thus, the direct comparison of NONMEM's OFV i to mapbayr's OFV i (OFV i,MBR ) could not be performed. A NONMEM-like OFV i (OFV i, NM-like ) was recomputed using ̂ i,NM with the same formula as in Section 2. 1 to ensure comparison to OFV i,MBR.
Package features mapbayr strongly relies on features provided by mrgsolve, a package to perform simulations from PK or PK/PD models. Notably, mrgsolve was intentionally designed to be similar to NONMEM in terms of model coding, with code blocks written in R/C++ with a syntax close to that of NONMEM's FORTRAN. Thus, if a user is familiar with NONMEM, it is rather straightforward to transpose a model into mrgsolve, unlike other available packages such as RxODE or nlmixr. 21, 40 rgsolve also accepts data passed into a NM-TRAN format. Overall, mapbayr was built around mrgsolve to perform MAP-BE from a mrgsolve model and includes the following features: • MAP-BE with R only. An additional software such as NONMEM, or MATLAB is not needed. • MAP-BE with every type of structural model accepted in mrgsolve, including time-varying covariates and nonlinear PK.
my_model <mread("path/to/my/model/ code. cpp") # a mrgsolve model my_data <read. csv("path/to/my/dataset. csv") #a NM-TRAN dataset my_est <mapbayest(my_model, my_data) Additional features also include functions to enter information about administrations and observations so that the data set is automatically formatted to NM-TRAN format as well as methods to plot and summarize the results of estimation. These functions deal with the burden of formatting data and plotting results so that the programmer can focus on functions specific to the drug and TDM protocol. To that extent, model and estimation objects are meant to be exploited by functions coded by the user to derive the PK outcome of interest (AUC, simulated concentrations, dose proposal, etc. ) and to be included into a Shiny application (Figure  1 ).
Each line represents 1000 estimations with an associated performance score: excellent if Δ ̂ i < 0. 1%, discordant if Δ ̂ i > 10%, and acceptable in between. Dashed line indicates 95th percentile than 0. 2 μM at the first sample, or concentrations higher at 48 h than 24 h postdose. For voriconazole, trough concentration predictions were discordant for 6/1000 patients only, despite the structural complexity of the model and the very large variance set on Michaelis-Menten elimination parameters (shared eta with 2 k = 1. 39). For the other real models, 100% rates of "excellent" estimation (difference <0. 1%) between mapbayr and NONMEM were achieved. The proposed dose to reach an AUC of 24 mg•min/ml for carboplatin (Run 901) ranged from 0 to 6000 mg. The steady-state trough concentrations of pazopanib, cabozantinib, and sunitinib + NDsunitinib were estimated identically by both software (median values 19 mg/L, 0.
DISCUSSION A package that enables the MAP-BE of PK parameters in R was developed, and its prediction capacity was validated against the gold-standard software NONMEM. The performance of mapbayr is overall equivalent to NONMEM's for a wide variety of test models. Most of the discrepancies were observed when large IIVs were investigated (Runs 501 to 513), notably when 2 k = 2 on all parameters (Run 513). This is probably because large variances cause a flatter surface of the likelihood, which makes minimization difficult. However, in real-life situations, such high discordance rates are unlikely because the current test scenarios probably returned abnormal PK profiles due to sampling from very large distributions for a majority of parameters. Indeed, the reanalysis of test model data without censored concentrations (0.
However, in other situations (Michaelis-Menten elimination, large IIV), differences between mapbayr and NONMEM were often in favor of the latter. The different algorithms used (L-BFGS-B vs. BFGS) and their associated settings probably explain these discrepancies in estimation. The hypothesis of differences in concentration prediction that would distort the computation of OFV i is less likely. Both software use the LSODA ODE solver to predict concentrations, and its C++ implementation in mrgsolve was found to be consistent to NONMEM's given the performances provided by mrgsolve. 41 F I G U R E 4 Performance with seven real models on parameter (left) and specific PK outcome (right) estimation. Dashed line indicates 95th percentile. AUC, area under the curve of concentrations versus time; AUC τ,SS , AUC at steady state between two doses; C24ss, trough concentration at steady state Estimation of parameters for real models (901 and more) returned a high percentage of patients with excellent performance despite a higher complexity in both structural and error models.
These questions of design optimality can be addressed with specific software (notably the R packages PFIM  44  or PopED  45  ) or more pragmatic approaches such as validation on a clinical data set. There was no proper comparison of run times of mapbayr versus NONMEM; however, there is no doubt that the first is slower because it was mainly written in R language, which is known to be slower than FORTRAN. Computation time increased with the number of parameters estimated: from milliseconds per individual for simple models with three to four parameters, to a couple of seconds for complex models with 12 parameters such as ibrutinib. Hence, maybayr's run times are fully compatible with routine use in clinical practice. Although some difficulties in the generalization of MIPD remain to be overcome (e.
g. , for methotrexate). Alternative dosing schedules can also be simulated from a posteriori individual PK parameters to predict dose adjustment and manage drug exposure. Several examples of shiny applications that include these procedures are provided in a dedicated repository at https://github. com/Felic ienLL/ mapba yr-shiny. Currently, estimations performed by mapbayr are only maximum a posteriori, which means most probable parameter values without taking uncertainties of estimation into account. Recently, Bayesian data assimilation methods have been suggested to overcome this limitation in the context of TDM. 48 Future versions of mapbayr could implement procedures based on the Fisher information matrix or on sampling from the posterior distribution. As a free and open-source package, mapbayr is developed on Github and available on Comprehensive R Archive Network.
Users are welcome to suggest features, report bugs, and participate in development at https://github. com/Felic ienLL/ mapbayr. In conclusion, instead of providing a ready-made application that would only be applicable for a limited number of users and clinical situations and mimic existing MIPD software, we developed a general tool to deal with the most sensitive step of model-based TDM: the MAP-BE. With mapbayr, anyone familiar with R can transpose any of his/her own models into a Shiny web application to perform TDM with a reliable MAP-BE of individual parameters without external modeling software. We hope this approach will help the development of MIPD, especially in less-considered therapeutic areas such as oncology.
). No data about the repartition of the sources used was recorded in this study. The report was compared to prescriptions recorded at the time of admission in order to identify potential discrepancies. Second, during the hospital stay, a medication review was performed by physician and pharmacist for all ongoing treatments. Further to this assessment, a pharmacist and a physician optimized the patient's drug treatments if needed, which may or may not have led to treatment change(s). Third, on discharge, the consistency between the discharge-prescription given to the patient and the decisions taken regarding her/his treatment during hospitalisation was examined; MR-induced treatment changes were performed when discrepancies were identified.
Data collection On admission, information on gender, age, ongoing medications (molecule and number) prior to admission, and patient's admission pattern and the context before hospitalization in internal medicine unit were collected. During hospitalization, we completed these by identifying all the professionals contributing to the out-hospital health management of the admitted patients; these were later contacted to complete and confirm information on ongoing drug treatments (type, dosage, indication). The patient's knowledge of her/his treatment was evaluated for each medication on an internal scale of 0 (null) to 4 (excellent) [see appendix 1]. This scale covers four knowledge-related criteria: e. g. , medication (commercial name or molecule), dose regimen, medical indication and the main adverse reactions.
Study Design To further elucidate the various corneal nerve changes associated with dry eye diseases, a retrospective nested case-control study was performed to assess the clinical features of patients suffering from primary MGD and AIDE and severe corneal pain, compared with their respective painless counterparts. Ultrastructural corneal characteristics were studied among patients presenting NCP characteristics (high pain score and absence of clinical signs) and compared with their painless counterparts and healthy volunteers. The study flowchart is summarized in Figure  1. This study included 921 patients with various ocular surface diseases. All patients had systematic pain history and ocular examination as part of their routine workup, and the clinical data were systematically collected in a secure spreadsheet (in adherence with national guidelines on data collection and protection).
Statistical Analyses Statistical analyses were performed using SPSS v. 23 (SPSS Inc. , Chicago, IL, USA). The normal distribution of the variables was tested independently in each study group using the Kolmogorov-Smirnov test. Parametric variables were compared with a t-test, nonparametric variables with Mann-Whitney U test, and nominal variables with χ 2. Graphical representations were made using GraphPad Prism v. 9 (GraphPad Software Inc. , La Jolla, CA, USA). Data were expressed as mean ± SD.
Statistical analyses Statistical analyses were performed using SPSS v. 23 (SPSS Inc. , Chicago, IL, US The normal distribution of the variables was tested independently in each study gro using the Kolmogorov-Smirnov test. Parametric variables were compared with a t-t non-parametric variables with Mann-Whitney U test, and nominal variables with Graphical representations were made using GraphPad Prism v. 9 (GraphPad Softw Inc. , La Jolla, CA, USA). Data were expressed as mean ± SD.
" Metribuzin's primary target organ is the liver based on an extensive regulatory toxicology dataset. In addition, effects in the thyroid in the rat were observed in the long-term repeat dose studies. As part of the reregistration process, all the available data relevant for the thyroid effects induced by metribuzin have been assessed to determine whether or not this active ingredient would qualify as an ED with respect to the thyroid modality. An ED assessment also requires the evaluation of estrogen, androgen, and steroidogenic modalities, however, no parameters associated with those were affected by metribuzin and they are accordingly not further discussed in this manuscript. Metribuzin's potential for thyroid effects was investigated in a set of experiments, comprising existing and novel studies conducted for reregistration purposes.
While the thyroid system is relatively conserved between species, there are pronounced physiological differences. Rats are particularly sensitive towards thyroid hormone modulation as compared to humans, due to differences in 1) serum protein pattern and associated plasma binding, 2) half-lives of thyroid hormone levels, and 3) thyroid hormone excretion/elimination pathways  (Choksi et al. , 2003; Colnot and Dekant, 2017; Hayes, 2014; Jahnke et al. , 2004). For metribuzin, the key questions that arise from the toxicological database in relation to thyroid effects and identifying potential for endocrine disruption are: • Does metribuzin alter functions of the thyroid system? • Does metribuzin show adverse effects in the thyroid? • Are these effects caused by an endocrine mode of action? • Are the observed effects human relevant? Metribuzin shows a specific T4 profile with serum T4 increases at low doses and T4 decreases at higher doses.
Overview of liver-and thyroid-related findings in the regulatory studies A full regulatory dataset according to the requirements for active substances to be used in plant protection products (European Commission, 2013) is available for metribuzin, see Rapporteur Assessment Report available from EFSA. 2 However, only the studies relevant for the evaluation of the thyroid effects are reported here (Table  1 ). The most relevant studies in rats are a 4-week gavage study with 2-week recovery (4wG), a 9-week dietary study (9wD), a 13-week dietary study (13wD) a pubertal assay (Pub) and a chronic toxicity and carcinogenicity study  (ChrD). No thyroid data were generated in the multi-generation rat studies. The main target organ in the toxicology studies was the liver, with effects on the thyroid correlating with liver effects, such as liver enzyme activity, increased liver weight or histopathological changes of the liver.
Study results on the thyroid MOA of metribuzin Description of materials and methods of the MOA studies is provided as supplementary data. The previous section showed that hypothesis 1, which proposed that low dose T4 increases are of spurious occurrence in the dataset, has to be rejected as increased T4 was consistently observed at doses between 30 and 100 ppm in studies of different designs and duration. However, both hypotheses 1 and 2 proposed an induction of UGT at high doses, which would explain the decreased T4 levels and histopathological observations in thyroid. The effect of metribuzin on UGT activity is explored in this section, both in an in vivo study and an in vitro study investigating species sensitivity, as suggested by the ED guidance.
Statistical Analysis Experiments were performed in triplicates and data are expressed as the means ± standard deviation (SD). The concentration giving 50% inhibition (IC 50 ) was calculated by nonlinear regression using the GraphPad Prism version 8. 0 (GraphPad Software, San Diego, CA, USA). ANOVA followed by Dunnett's test was applied to evaluate the differences between data and positive control results in biological assays. Tukey's test was used to determine any significant difference between all the treatments at different levels. The Relative Antioxidant Capacity Index (RACI) was used to establish the antioxidant potential  [26].
Objective: We aimed at testing if a correlation between adverse drug reactions relative risks estimated from meta-analyses and disproportionality analyses calculated from pharmacovigilance spontaneous reporting systems databases exist, and if methodological choices modify this correlation. Study design: We extracted adverse drug reactions (ADR) odds ratios (ORs) from meta-analyses used as reference and calculated corresponding Reporting Odds Ratios (RORs) from the WHO pharmacovigilance database according to five different designs. We also calculated the relative bias and agreement of ROR compared to ORs. Results: We selected five meta-analyses which displayed a panel of 13 ADRs. A significant correlation for 7 out of the 13 ADRs studied in the primary analysis was found. The methods for ROR calculation impacted the results but none systematically improved the correlations.
Introduction Disproportionality analyses are statistical methods widely used by national drug agencies, industries, and researchers for safety signal detection in pharmacovigilance spontaneous reporting systems databases. They aim at quantifying the extent to which a drug-event pair is reported more often than it would be at random. A signal of disproportionate reporting (SDR) is a statistical association that does not imply a causal relationship between the administration of the drug and the occurrence of the adverse event  [1 , 2]. Generally, disproportionality analyses cannot be used to assess a drug-relative risk because they do not provide risk quantification  [3]. Nevertheless, Maciá-Martinez et al. found a significant correlation between disproportionality analyses and relative risks estimated in epidemiological studies and meta-analyses  [4].
Pharmacovigilance database VigiBase is the World Health organization's (WHO) global safety database of individual case safety reports (IC-SRs). The database contained over 20 million ICSRs from 131 member countries and 29 associated members of the WHO Programme for International Drug Monitoring. Vi-giBase provides ICSRs with patient information such as gender, age, medical history, country, drug and concomitant drug taken with chronological information, indication of the drug, adverse effects, and their severity and outcome  [15].
Disproportionality analyses Several disproportionality methods have been developed and are currently applied in pharmacovigilance databases, but no gold standard in terms of performance, accuracy and reliability has been established  [19 , 20]. In the present study, disproportionality analyses were performed using the Reporting Odds Ratio (ROR) method, with 95% confidence intervals (CI)  [21]. Given the poor accuracy of the ROR calculated with less than three individual cases, drugadverse effect pairs for which the number of cases was lower than 3 were excluded from analysis  [13 , 20 , 22]. To explore factors that may influence the correlation between RORs and ORs, several disproportionality analyses were performed in Vigibase. A primary disproportionality analysis was performed for each drug or drug class considered as suspect at the publication date of the corresponding meta-analysis, to minimize the notoriety bias and the modification of reporting rates over time.
05). [31]  As suggested by a reviewer, we explored the association between ADR characteristics and correlation coefficients through nonparametric Kruskal Wallis tests. Median and standard errors are presented, a P -value < 0. 05 was considered significant given the exploratory nature of these analyses. We also assessed the relative bias of ROR compared to OR. We thus calculated the prediction errors between the two estimates divided by the ROR value; using this formulae RB = ( │pred-obs │)/pred; pred = predicted value (ROR); obs = observed values (OR)  [32]. Lastly, we calculated the agreement between OR and ROR through Bland-Altman method  [33]. All analyses were performed using R statistical software (Version 3. 3. 0).
Discussion To our knowledge, this is the first study exploring the impact of disproportionality analyses study design for ROR calculation on the correlation with ADR relative risks estimated from clinical trials. We found a significant correlation for 7 out of the 13 ADRs studied in the primary anal-ysis. In the case of no data from epidemiological studies or clinical trials exist, because the ADR is a new signal or is difficult to assess by these methods (eg, extremely rare or very long time to onset), using pharmacovigilance database to hierarchize a risk among a drug class could be of interest. Those disproportionality results could subsequently be used in routine pharmacovigilance activities, to design pharmacoepidemiological studies or to explore ADR mechanisms by correlating the results with drug-receptor affinity, physico-chemical or pharmacokinetic properties.
However, the impact of methodological choices for data synthesis, sensitivity analyses (eg, excluding high risk of bias trials or non-serious ADRs) or adjustment on confounding factors through metaregressions on ADR relative risk estimation in meta-analysis and on the correlation with other source of data need further research. Pharmacovigilance databases are known to be mainly helpful in detecting type B adverse effects (ie, effects that are often allergic or idiosyncratic reactions, characteristically occurring in only a minority of patients and usually unrelated to dosage and that are serious, unexpected and unpredictable), as well as unusual type A ADRs  [44]. All ADRs in this study were type A (ie, pharmacologicallyexpected and dose-related adverse effects), except suicide with antidepressants, thus further research is needed to assess our findings in different types of ADR.
The instrument was equipped with a DB-5MSITD column (30 m × 0. 25 mm, film thickness 0. 25 μm) (J&W Scientific, Folsom, USA) with helium used as a carrier gas. The column was heated from 35 • C (1 min hold) to 300 • C at 5 • C min -1 rate (15 min hold). Mass spectra of the transformation products were recorded in the full scan acquisition mode (mass range 33-650 m/z) and the scan rate of 1 scan s -1. The injection volume of the sample was 2 μl. Given that the vast majority of the TPs did not exist within database libraries (NIST, Wiley), their identification was performed by a comprehensive examination of the fragmentation patterns of the mass spectra ions.