dataesr/scientific-paragraphs-categorization huggingface dataset:
More details are provided in the next section. Systemspecific (physiological) parameters were fixed to the default value in the PK-Sim software, while for each drug a prespecified set of drug-specific parameters were either estimated from the mouse plasma PK data or fixed to published literature values (Fig. 2 ). Table  2  provides an overview of the drug-specific parameters (i. e. physicochemical properties, protein binding) and the values to which these parameters were fixed. Mouse protein binding was assumed to be similar between mouse strains (C57BL/6J, BALB/c, CD1) and fixed to experimental values obtained in our labs. Finally, as in vivo doses were originally reported in mg/kg, the actual dose in mg was calculated assuming that each mouse weighed either 20 g (C57BL/6J and BALB/c) or 25 g (CD1).
Estimation of the remaining drug-specific parameters in the PBPK model The Monte-Carlo algorithm in the PK-Sim software was used to estimate the relevant PBPK parameters. As described previously, the proposed approach for model building consisted in the use of IV PK data to estimate the lipophilicity and characterise the distribution properties of the drugs. Estimation was performed using all five distribution models, as implemented in the PK-Sim software. Given that lung tissue concentrations were considered for validation purposes only (rather than informing selection of the best distribution model), the goodnessof-fit for the predicted plasma concentration vs. time profile was evaluated for each distribution model. The best fit was used as criteria for selection of the distribution model.
Details on the difference between each distribution model (Berezhkovskiy, PK-Sim, Poulin and Theil, Rodgers and Rowland, Schmitt) have been published elsewhere  (Kuepfer et al. , 2016). The lipophilicity estimates for each compound were then fixed and the model re-run with inclusion of the oral PK data. During this step, the transcellular intestinal permeability was estimated to describe the absorption processes of the drugs. Drug metabolism was described with first-order total hepatic clearance, with the exception of PZA. For PZA, an initial evaluation of the oral data suggested nonlinear PK. However, as the data collected after IV administration was based on a single dose level, lipophilicity estimates were obtained assuming first-order elimination. Saturation kinetics was tested subsequently with a Michaelis-Menten process after inclusion of the data from oral administration.
Lung biopsy data in tuberculosis patients The predictive performance of the PBPK models for RIF, INH and PZA in humans was assessed using lung biopsy data from the published literature. Blood samples and tissue biopsy were collected from 15 Asian multidrug resistant (MDR)-TB patients who underwent lung resection surgery (Fig. S2 )  (Prideaux et al. , 2015a). In this study, all patients concomitantly received a single dose of INH (300 mg), RIF (600 mg; 450 mg when patient weighed <50 kg), 1500 mg PZA and 400 mg moxifloxacin (MXF). No drug-drug interaction between these drugs was anticipated. All drugs were administered at 2, 4, 8, 12 or 24 h prior to surgery. Some patients were already at state levels for INH (N = 3; 20%) and/or PZA (N = 4; 27%) as they had received these drugs as part of their background drug regimen for several weeks or months before surgery.
Virtual population and study design for the assessment of the predictive performance of the PBPK model in tuberculosis patients To mimic the population in the reference lung biopsy study, a virtual Asian population consisting of 1500 subjects (60% male) was first simulated using the baseline demographic characteristics (e. g. median and range values) from the study by  Prideaux et al. (2015a)  and the PK-Sim Asian population database  (Tanaka and Kawamura, 1996). Given that most observed INH and PZA concentrations in the study by  Prideaux et al. were derived following single dose administration, concentration vs. time profiles in plasma and lung tissue were simulated following a single dose administration of 600 mg RIF, 300 mg INH, 1100 mg EMB and 1500 mg PZA to mimic the original study design.
Evaluation of the predicted lung tissue exposure in tuberculosis patients Given the sparse sampling schedule used in the Asian patient population (Fig. S2 ), AUC 0-τ in plasma and lung tissue could not be calculated. Instead, simulated concentration profiles (in plasma and lung tissue) were superimposed to the observed data using the selected distribution model. These profiles take into account the contribution of demographic covariates, with baseline characteristics matching those of the original population in Prideaux et al. (2015a) (Fig. S3 ). Median age, weight and body mass index (BMI) of the virtual population were respectively 41 years (range, 23-59), 58. 6 kg (range, 49. 8-83. 6) and 22 kg m -2 (range, 17. 4-29. 3). Albeit limited, the predictive performance was subsequently assessed by comparing predicted and observed profiles.
Software PBPK models were built using PK-Sim 6. 2, which is currently part of the Open Systems Pharmacology software package  (Lippert et al. , 2019). WebPlotDigitizer was used to extract data from figures in the publications  (Rohatgi, 2020). All other steps relative to data preparation, formatting and graphical analysis were performed in R v3. 2. 5 (R Core Team, 2020). No formal statistical hypothesis testing other than the pre-defined diagnostic criteria was used for the evaluation of the predictive performance of the models.
Limitations We acknowledge that we have not exhausted all options that in traditional settings would have been explored to optimise the PBPK models (i. e. inclusion of in vitro data and PK data from other species such as rat, monkey, or dog to inform IVIVC) prior to human PK predictions. Such traditional data-driven PBPK modelling approach was beyond the scope of our investigation, as this exercise attempted to provide insight into the predictive performance of a simplified PBPK model building strategy based only on plasma PK data from mouse to be implemented during lead optimisation. At this stage of drug development, in vitro data (e. g. , Caco-2 permeability, microsomal clearance) and PK in multiple (non-rodent) species have yet to be generated. On the other hand, whilst the use of in vitro human data (such as hepatocyte or microsomal clearance or Caco-2 permeability) and PK data from multiple species could have improved the predictions, such an evaluation would only be feasible at a much later stage of development.
Acknowledgements This work was supported by the  Interdisciplinary Thematic Institute SysChem via the IdEx Unistra  ( ANR-10-IDEX-0002 ) within the  program Investissement d'Avenir. N. Z. thanks the  EU  for a  H2020 Marie Skłodowska Curie Fellowship  ( 896219 ). S. B. thanks the  CSC Graduate School  funded by the  French National Research Agency  ( CSC-IGS ANR-17-EURE-0016 ). R. J. M thanks the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) for a fellowship ( MA 9687/1-1 ). Computations were performed at the  High Performance Computing Center of the University of Strasbourg. Part of the computing resources were funded by the  Equipex Equip@Meso project  ( Programme Investissements d'Avenir ) and the  CPER Alsacalcul/Big Data. The authors thank  Wahnyalo Kazone  for HRMS analysis.
Solid dispersion requires additional excipients such as polymers or surfactants to maintain its metastable state. The additional excipients may introduce higher pill burden and necessitates the administration of a large tablet or multiple small tablets, pellets, or granules. The additional excipients should also meet the regulatory requirements and be suitable for the paediatric patient population. Their respective impurities must be within acceptable level for the paediatric patient population. The use of excipients that are classified as GRAS based on FDA's inactive ingredients database (IID), in paediatric formulations may require additional due diligence. The FDA's IID provides a list of GRAS excipients and their respective acceptable levels, but does not specify the use for paediatric and non-paediatric indications.
The Lipid Formulation Classification Scheme Consortium generated a large database describing the behaviour of Type I-IV formulations and used these data to create lipid formulation-performance classification system (LF-PCS) that grades lipid-based formulations based on in vitro performance in dispersion and digestion tests  [122]. Looking at these different types of lipidic systems under the aspect of paediatric drug delivery, the ''landscape" of marketed lipid products seems to be quite diverse and rather complex. It is probably difficult to define clear rules for the different drugs, required dosage strengths and type of systems. In a few cases, it is obvious that pharmaceutical companies have developed alternative liquid dosage forms or solid forms to cover paediatric needs. Examples of selected commercialised lipid-based formulations for adults and paediatric as per the classification is summarised below.
For a good detection of potential DDIs, a more contextualized definition of DDIs is also important and limits false positives  (5). The context can be divided into drugrelated factors (e. g. dosage, route of administration, formulation) or patient-related factors (e. g. comorbidities, elimination phenotypes)  (6)  but experiments of contextualized detection have been only performed on a few DDIs. In this paper, we use a unique prescription dataset from the Rouen University Hospital data warehouse  (7)  to test the impact of different contextual items on the DDI frequency measurement.
Identification of the manufactured pharmaceutical products potentially eligible for a DDI. An initial list of Proprietary Medicinal Products (PMP) marketed in 2017 was compiled with the available data in the Répertoire des Spécialités Pharmaceutiques (http://agence-prd. ansm. sante. fr/php/ecodex/). These PMP were identified by their specific French code name "Code Identifiant de Spécialité" (CIS, Proprietary Medicinal Product identifier). The French Thesaurus of DDIs (8) was the reference material for contra-indicated DDI. These DDIs were manually analyzed by two pharmacists to identify the context attributes that might narrow the scope of a DDI to a subset of either substances, or PMPs or patients. The PMPs description was enriched with the attributes of interest through the information contained in their Summary of Product Characteristics.
Data extraction process Medications electronically prescribed for patients hospitalized in 2017 in Rouen University Hospital were extracted from EDSaN data warehouse in accordance with current regulations on health data privacy (anonymous, partial, aggregated data with a minimum threshold of ten). For reasons of parsimony, only daily prescription lines containing a PMP likely to be involved in a DDI were included in the study. As drugs were identified by their French code name "Unité Commune de Dispensation" (UCD, common dispensation unit), a mapping between the CIS and the UCD was performed using the multilingual terminology server HeTOP (9) which integrated the UCD repository provide by the French Agency of Digital Health (https://esanté. gouv. fr/) (10). The metadata extracted were: the anonymous patient identifier, the anonymous hospital stay identifier, the anonymous daily prescription identifier and the drugs identified by their UCD.
Analysis The number of DPs was calculated on the whole 2017 prescriptions and was used as the denominator of the frequency indicators. For each DP, all drugs pairs were generated and compared to the list 1 of PMPs pairs. Only the DPs containing at least one PMPs pair involved in a DDI were retained and their number was calculated. The number of DPs with DDIs was then recalculated by applying the filters corresponding to each studied context to the prescription dataset. These numbers are the numerators of the frequency indicators. The percentage of false positives was defined as the number of falsely detected PMPs pairs out of the whole detected PMPs pairs.
Of the remaining 102,175 PMPs pairs (gold standard), 14,140 could be further filtered using patient data such as history (e. g. history of gastrointestinal ulcer), or biology (e. g. hypokalemia). Of the 5,032 expected CIS, 4,840 were mapped to an UCD using HeTOP. The lack of mapping was due to a lack of coverage of the repository provided by the French regulatory authority. The dataset extracted from EDSaN contained 916,584 DPs for 2017. Within these prescriptions, only 1,963 of the UCDs were of interest and only 333 of these were cooccurring as a pair of DDI. In the end, only 990 different PMPs pairs from the list 1 were identified. Depending on the different context filters to be applied, the frequency of DPs with a DDI varied as presented in Table  1. Without any filter, the percentage of DPs with a potential DDI was 3.
Discussion and Conclusion In the reported experience, we showed that, with the same dataset, the measurement of the frequency of potential DDIs can vary with a fourfold, depending on the application of contexts items described in the DDI thesaurus. The right definition of what drugs should be included in a DDI is a first statement. These drugs cannot be solely defined by their substance or ATC class because it is sometimes too broad when forms or dosage matter. This would lead to an overestimation of the frequency of DDIs. The generic drug definition (substance + dosage +form) is sometimes not suitable when excipient or indication matter. This would lead to an underestimation of the frequency of DDIs. PMP seems to be the right level of analysis, but its scope is national which makes international comparisons difficult.
Acknowledgments This study was partially granted by (a)  QLAP (Quantification des perofmances des logiciels d'aide à la prescription pour la détection des intéractions médicamentueses) ANSM -Appel à projet 2016 -Projet 7, (b) ReMIAMes (Ressources et méthodes libres pour la détection des interactions médicamenteuses dans les entrepôts de données de santé )  ANR-19-CE19-0025-01.
Ziziphus nummularia, a small bush of the Rhamnaceae family, has been widely used in traditional folk medicine, is rich in bioactive molecules, and has many reported pharmacological and therapeutic properties. Objective: To gather the current knowledge related to the medicinal characteristics of Z. nummularia. Specifically, its phytochemical contents and pharmacological activities in the treatment of various diseases such as cancer, diabetes, and cardiovascular diseases, are discussed. Methods: Major scientific literature databases, including PubMed.
Methods Major scientific literature databases, including PubMed, Scopus, ScienceDirect, SciFinder, Chemical Abstracts, Medicinal and Aromatic Plants Abstracts, Henriette′s Herbal Homepage, Dr. Duke′s Phytochemical and Ethnobotanical Databases, were searched to retrieve articles related to the review subject. General web searches using Google and Google scholar were also utilized. The search period covered articles published between 1980 and the end of October 2021. The search used the keywords and MeSH terms for 'Ziziphus nummularia', AND ('phytochemical content', 'pharmacological properties, or activities, or effects, or roles', 'anti-inflammatory', 'anti-drought', 'antithermal', 'anthelmintic', 'antidiabetic',' anticancer', 'anticholinesterase', 'antimicrobial', 'sedative', 'antipyretic', 'analgesic', or 'gastrointestinal').
Strengths and limitations of this study ► This is the first time the Transparency in Healthcare database was used to analyse the links between key opinion leader (KOL) and the industry. ► The authors cross-checked the nationwide database of financial ties with three databases of professional medical associations. ► All medical doctors practising in France were included, with a focus on 548 KOLs defined as board members of all the professional medical associations that published clinical practice guidelines in 2018 or 2019. ► The main limitation of this study arises from the quality of information provided by the French Transparency in Healthcare database. ► The definition of KOLs used here is somewhat restrictive and further research is needed to better understand the links between KOLs and the industry.
Open access created the Transparency in Healthcare public database (transparence. santé. gouv. 5] [16]  Pharmaceutical and medical device industries are required by law to disclose the value of gifts, contractual agreements and remunerations they transfer to healthcare professionals in France. In this database, 'Gifts' include anything that is granted without consideration, in kind or in cash, directly or indirectly, with a value greater than or equal to €10 (US$11. 4) including taxes. 'Remunerations' represent the payment by companies for work or services with a value greater than or equal to €10. 'Contractual agreements' involve obligations on both sides: participation in a congress, research or clinical trial activity, training action, etc. For more convenience, this paper will gather both pharmaceutical and medical device industries under the term 'pharmaceutical industry'.
Identifying professional medical associations Professional medical associations were defined as any group of physicians who publish clinical practice guidelines in France. One author (MC) built the list of eligible associations by cross-checking three different databases: the 'Bibliothèque Médicale AF Lemanissier' (BMLweb),  25  the 'Catalogue et index des sites médicaux de langue française' (CISMEF), 26 and the 'Le Parisien' catalogue of professional medical associations. 27 We included only national associations and excluded association titled as concerning a 'rare disease'. The BML website is an academic medical library that lists month by month all the consensus statements, guidelines and recommendations published in French. MC conducted a search through it from January 2018 to December 2019 and selected all the national professional medical associations regardless of the nature of the publications they were listed for.
The next step was to examine the Cismef website, which has a 'learnt society' section that lists French speaking professional medical associations. MC selected all the professional medical associations in France from that list. Finally, MC used the 'Le Parisien' database that lists French learnt societies, and selected all the professional medical associations in the 'medical science' section. Duplicates were then eliminated and MC examined the associations one by one to determine whether they had published guidelines in 2018 or 2019. To do so, BMLweb was used fist and then the search engine for clinical practice guidelines of the Cismef website if there was no match on BMLweb, and finally Google Scholar and the association website.
Identifying KOL Using each professional medical association's website, MC identified between October 2018 and May 2020 all the physicians who were board members. KOLs were defined as members of the association's board or governing council but not of sub-committees. KOLs were identified by name, medical specialty and city of practice via the medical association website and if missing on Google. Discrepancies and uncertainties were resolved by discussion with a second author (AB). The Transparency in Healthcare database was downloaded on 18 May 2020 from the EurosForDocs 28 website. EurosForDocs is a tool inspired by the American website DollarsForDocs. EurosForDocs aims to help browsers find and understand information in the Transparency in Healthcare database by cleaning and grouping payments by categories and beneficiaries.
Identifying and extracting payment details By using the RPPS unique identification number, data regarding payments to the identified leaders 29 were extracted using the database categories: gifts, contractual agreements and remunerations. We took the data into consideration starting from the date on which their declaration became mandatory: gifts from 1 January 2014 to 31  Open 2022;12:e051042. doi:10. 1136/bmjopen-2021-051042 Open access December 2019 and contractual agreements and remunerations from 1 January 2017 to 31 December 2019. 3 Clinckemaillie M, et al. BMJ The characteristics and date on which declaration of the payments became mandatory are presented in table  1.
Changes to protocol The secondary outcome concerning contractual agreements and remunerations was not part of the protocol as these declarations were not mandatory before 2017. However, after having observed that the value of remunerations represented more than three times the yearly value of gifts, it was decided to include contractual agreements and remunerations because without them, an important part of physician-industry ties would have been missed. We identified some outliers with implausible amounts which seemed to indicate that some of the information in the database contained errors (eg, some gifts may have been reported in cents by the company (outliers typically ending in two zeros)). It was therefore decided a posteriori to exclude amounts exceeding €100 000 (US$118 000) for a single payment.
Participants We identified 238 professional medical associations. A total of 101 of them had produced clinical practice guidelines in 2018 and/or 2019 and two of them had no website or did not describe their board on their website. We identified 605 KOLs. 548 of them were found on the Transparency in Healthcare database. The number of KOLs in each professional medical association ranged from 1 to 12, with a median of 6. 12 KOLs belonged to more than Open access one professional medical association. The way KOLs were identified is described in the figure  1.
Transparency in Healthcare public database The database reported financial ties totaling €6B (US$7. 1B) over 8 years. Gifts accounted for €1. 7B, contractual agreements for €1. 3B and remunerations for €3B. 28 Gifts, contractual agreements and remunerations are presented below starting from the year in which they were consistently declared, that is since 2014, 2017 and 2017 respectively.
The number and value of gifts, contractual agreements and remunerations for all the members of a single association varied widely from one association to another. The number, value and proportion of gifts, contractual agreements and remunerations for KOLs slightly decreased over time. Remunerations represented the largest amount declared for KOLs with a median amount per capita four times higher than for other physicians. KOLs represented 0. 24% of the physicians but were associated with 1. 5% of the gifts, 2. 4% of the contractual agreements and 4. 4% of the remunerations in value. This represents €3700 in gifts, €1900 in agreements and €4100 in remunerations per capita per year. The amount for contractual agreements is probably underestimated since 79% of KOL agreement amounts were declared null in the database (see above).
Strengths and limitations This study is exhaustive of all ties declared in the French Transparency in Healthcare database. All physicians practising in France were included since declaration of industry ties is mandatory. However, the results may be underestimated since many amounts for contractual agreements were not available. One reason for this is that when a physician signs an agreement conferring an advantage, the amount can be declared nil as a contractual agreement or a gift, or declared as both an agreement and a gift. There is no government control at this level. The effect of this bias is difficult to predict. Either firms did not declare the amount of thousands of contractual agreements, thus underestimating the amounts received by physicians or the amount of an agreement could have been counted twice.