dataesr/scientific-paragraphs-categorization huggingface dataset:
This has driven the creation of a database to store and share such validated information. The database, maintained by Lhasa Limited, constitutes a central platform to hold the data donated by the pharmaceutical company members on the nitrite concentrations in common excipients measured with validated analytical procedures. The goal of this data sharing initiative is to provide a common framework to contextualize and estimate the risk posed by presence of nitrites to contribute to the formation of nitrosamines in drug products. The major findings from the database analyses are: (1) average nitrite content and batch to batch variance differ among excipients, (2) for solid dosage forms, the nitrite contribution is dominated by the highest formula % excipients, e. g. , the fillers (diluents), which are typically used in larger proportion, and are characterized by low nitrite levels and low variability, leading to an average value of 1 mg/g nitrite in a typical formulation, (3) substantial differences in average nitrite content in batches from different excipient vendors potentially reflecting differences in source materials or processing methods for excipient manufacturing.
Primary packaging materials, and potential contaminations are also relevant as they might -as the substances mentioned before -trigger the formation of nitrosamines from amines by introduction of nitrite contamination during manufacture. The manufacturing processes of both drug substance and drug product as well as their stability are considered. 9 o date, very little information has been published about the presence and impact of nitrosating agent precursors in excipients, with only Wu et al. 10  in 2011 listing reactive components in some common excipients and McCarty  11  providing a case study based on this publication. Because nitrite in excipients can, under specific conditions, form nitrosating agents that can react with vulnerable (i. e. , secondary, but also tertiary) amines in drug formulations, a need for a large and readily accessible dataset on nitrite levels in common excipients was identified.
By exploring several variables such as the type of excipient it should be possible to define a framework for the assessment of the risk posed by nitrites with respect to the formation of nitrosamines in pharmaceutical products. Following the example of the well-established Elemental Impurities in Excipients database for the International Council for Harmonization (ICH) Q3D risk assessment  12, 13  founded by a consortium of pharmaceutical companies in 2016, a similar proactive initiative was launched to collect and share industry-wide representative data for nitrite levels in excipients. A consortium of pharmaceutical companies was established in 2020 under the umbrella of Lhasa Limited 13 to facilitate the data sharing. Lhasa Limited is a not-for-profit organization and education charity.
The generation of a comprehensive and robust dataset is particularly important since nitrite is typically not on any pharmacopeial excipient specification. While the idea and some groundwork of the database was initiated within the IQ Consortium, 1  the database build, coordination, and on-going work has been driven by Lhasa Limited with support from the IQ consortium nitrosamine working group. Nitrite analysis of excipients is a new and analytically challenging area with many laboratories working quickly to determine the best practices and techniques to quantify levels of nitrite. To assure the generation of high-quality data, the consortium agreed on analytical method validation criteria in line with ICH Q2 standards. Considering the redox dependency between nitrite and nitrate, and the possible conversion of nitrate to nitrite upon reduction, nitrate levels have also been collected and shared for some samples, but the focus of this publication is on nitrite results.
Database The Nitrite in Excipients database, hosted on Lhasa's Vitic database system,  15  allows users to query, review and export the data and prohibits the modification and deletion of data. The consortium has defined the data fields and the permitted values to ensure the correct level of detail and consistency across data records Table  1. Each member organization of the consortium carries out excipient testing, shares the data generated from the analyses and in return can access the data donated by all the members after curation in the standard database format. The data can be generated using any analytical method if the validation criteria are met. The validation status is reported in a dedicated field in the database. Data on nitrate levels, if available, are also collected in the database, but the focus is primarily on nitrite results.
Prior to entering data into the database, the supplier and batch information is blinded by Lhasa. This is an important step to keep the data focused on scientific purpose and not business interest. As a result, a limitation of the system is that it is impossible to select excipients based on their grade. However, the supplier and batch codes allow users to identify a duplicate batch or excipient sourced from the same supplier. Due to the high importance and demand for knowledge on nitrite levels, the database has grown quickly since its inception, as illustrated in Fig. 2. After the initial release of the database in November 2020, it has been updated quarterly. The Nitrites in Excipients database version 2021. 2. 0, used as basis for this publication, contains 497 studies covering a total of 71 unique excipients.
Plots Data analyses were performed in KNIME Analytics Platform version 3. 7. 0. 16 Density plots and box plots were created with the R Fig. 1. Example formation of a Nitrosamine from a secondary amine. Depending on conditions, the actual nitrosating agent is either the nitrous acidium ion, the nitrosonium ion or dinitrogen trioxide  2. Tertiary amines are less reactive than secondary amines  4  , therefore the focus here is on secondary amines. language for statistical computing  17.
Sample Preparation for Nitrite Impurity Assay in Excipients Table  1  shows the permitted values gathered on excipient nitrite sample preparation. To investigate in detail how the excipient testing was completed by the various organizations, some additional information on the analytical procedure was requested for nine excipients commonly used in solid oral formulations for which the database provided most results. These additional data amounted to 250 nitrite results and contain details of sample preparation (sonication, diluent, filtration). Table  2  provides a summary of analyses performed on the sample preparation details per excipient. Although this additional information was not entered in the database, it was valuable to help begin understanding variation in testing methodology per excipient and to observe any commonly used procedures applied.
Validation A common validation approach was agreed upon to ensure data consistency. This approach allows the use of any analytical method compliant with the criteria described in Table  3  for full validation. The LOD and LOQ are not validation criteria so each measurement can be different for each analysis donation and the LOQ are reported in the dedicated fields in the database. Either a reduced or full validation was performed. For the reduced validation, the same performance parameters were considered but with fewer replicates and/or levels. If neither the full nor the reduced validation were feasible, a limit test was performed. At this level, LOD and specificity were assessed. The choice of validation option is (a) Explanatory text and definitions are given in square brackets and do not form part of the field value.
Test Methods The two main analytical methods for nitrite determination were ion chromatography with conductivity detection (67% of records) and the Griess method  20, 21  of nitrite detection coupled with liquid chromatography and UV-detection (31% of records). The remainder of the analyses were done by Griess reaction with UV-visible spectroscopy alone. For the majority of the 71 excipients, there was no significant impact of the sample preparation and analytical methods on the nitrite levels (data not shown). Ion chromatography with conductivity detection allows the determination of nitrate, as well. The Griess reaction is specific to nitrite, therefore most contributors using this method did not report nitrate data. Some contributors, however, used methods involving reduction of nitrate to nitrite prior to the Griess reaction, making the detection of nitrate possible.
Excipient Coverage A review of novel drugs approved by the US FDA from January 2019 to August 2021  22  was carried out to confirm the relevance of the excipients in the database. Using the label information available for solid dosage formulations approved during this period, a list of commonly used excipients was created. Table  5  reports the top 20 excipients and for each of them the number of records in the database together with the number of formulations in which they were present in the label among the approved new drugs by US FDA over the 2019-2021 period. More than 80% of all results in the database can be attributed to excipients used in solid oral dosage forms.
Cross Laboratory Testing/Comparative Testing -Method Since neither a large amount of data nor well-established testing procedures were available in 2020, the consortium identified the need to perform some comparative nitrite testing across different laboratories. These analyses aimed to demonstrate the reliability of the data captured in the database, build confidence in the data sharing process, and support the validation guidance in development. Two excipients were chosen based on availability and the presence of nitrite. Two batches of crospovidone were sourced from two different suppliers and shared between the participating members. One batch of USP reference standard magnesium stearate was tested. Each organization was free to use their preferred sample preparation and analytical technique.
Nitrite Levels in Excipients Each of the database updates shows a trend of an increased number of results per excipient, due partly to the multiple member organization donating data on excipients sourced from multiple excipient manufacturers/suppliers. For example, the dataset for the excipient lactose monohydrate includes 34 results from eight suppliers. Where multiple batches for an excipient are present in the database, it is important to determine any variability across the results. Statistical parameters (minimum, maximum, median, mean, standard deviation and RSD) of the nitrite and nitrate (whenever available) levels are calculated per excipient and reported in the database summary table. 45% of nitrite results are numerical values, and 55% are either "LLOQ" or "Not detected". In the context of the summary statistical analyses, these non-numerical results are replaced by the associated LOQ or LOD for limit tests.
2 to 14 mg/g. The density plot in Fig. 4  indicates that while there are a few results above 10 mg/g, most are much lower, as evidenced by low average values of the median (1. 3 mg/g) and mean (1. 4 mg/g). 79% of Fig. 3. Box plots detail the results for the two crospovidone batches and the magnesium stearate reference batch. For the magnesium stearate, the results are split by analytical method. The %RSD has been added to the box plots. nitrite results are below 1 mg/g and only 1. 8% are above 10 mg/g. This low percentage of high values is often linked to excipients with only one high result in the database. Most of the results with nitrite levels >10 mg/g are valid results with a much lower LOQ and are the only result for a particular excipient in the database. In these cases, further data is required to establish whether the high nitrite levels are typical for these excipients.
This indicates that a) more data is required, and b) users of the database should carefully assess these values and consider performing testing themselves. Table  7  shows the statistical parameters calculated for the nitrite levels of the 8 excipients in the database with the most results and the number of suppliers the excipient has been sourced from. This summary of nitrite data is a snapshot of the nitrite levels as observed in the 2021. 2. 0 database version and will evolve as more data are collected for these excipients. Summary data can be used in combination with the detailed analytical results available in the database. In some instances, the maximum nitrite level observed for an excipient is the LOQ level so the actual concentration could be much lower, so review of each individual analytical results in the database is recommended.
The evaluation of the database has also shown that there can be differences between various suppliers as seen in Fig. 5. Fig. 6A  shows the distribution of the nitrite results for selected excipients which are typically used in solid oral formulations. Generally, the results show low levels of nitrite in these excipients and a tight spread of variation. Although a few nitrite results above 10 mg/g are observed, the vast majority are much lower. The contribution of each excipient to the overall nitrite level in a formulation is proportional to the ratio of that excipient in the formulation composition. In Fig. 6B  it is demonstrated for example solid oral formulation that the nitrite contribution is greatly affected by the overall excipient loading. Because some excipients (e. g. , fillers) are usually present in much higher amounts than others (e.
g. , lubricants, disintegrants), the overall nitrite content in a formulation may vary less than in any single excipient which is used at a low concentration. For a solid dosage form, for example, utilizing different combinations of excipients in their usual ranges,  23  an average nitrite content of 1 mg/g can be calculated. An example of a typical solid oral formulation and resulting nitrite content is shown in Table  8. The excipients with the higher nitrite values in Table  8  have a low impact on the overall nitrite level because of their low percentage among the constituents of this example formulation. Fig. 4. Density plot of nitrite levels (mg/g) for all 71 excipients in the database; where no numerical result was available the corresponding LOQ value was used. Vertical dashed lines show the median values of (from left to right) the groups min, median, mean and max.
Calculation of Nitrite Levels The aim of the database is to aid in compiling assessments for risk of nitrosamine formation in drug products. By understanding the distribution of nitrite content in an excipient (across lots and vendors), and the averaging effects when grouping multiple excipients in a formulation, one can use the most representative data for worse-case estimates. An additional factor in the risk assessment, which we have not covered in this paper, is the proper estimate for conversion of nitrites into nitrosamines in a solid formulation, which is expected to occur at a fraction of the possible stoichiometric conversion. 24 onsidering situations in which a conversion of available nitrite into nitrosamines might occur, for example in solutions at low pH, the maximum amount of nitrosamines formed per gram of formulation, can be calculated using Eq.
Conclusions The consortium has established a continuously growing database of nitrite for a wide range of pharmaceutical excipients, which is used by member organizations as a valuable tool to support Nnitrosamine risk assessment of drug products. Excipients covered in the database are representative for common formulations, as evidenced by comparison to recent formulations used in FDA novel approved drugs. Major findings from the presented database analyses are: (1) Average nitrite content and batch to batch variance differs among excipient types. (2) For most solid dosage forms, the nitrite contribution is dominated by the bulking agents, which have been found to contain the lower and least variable levels of nitrite, converging to » 1 mg/g nitrite averaged in a typical formulation. (3) Differences in average nitrite content in batches from different excipient vendors indicate differences in source materials or processing.
The database allows detailed searches as well as the extraction of statistical data for each excipient, allowing users to assess a specific excipient and its nitrite levels. Additional in-house testing can also be of value if information for specific grades of an excipient is needed. Nitrite results in the database have been generated by different preparative and analytical procedures, with the results showing that all methods yield acceptable data, if the validation criteria are fulfilled. No conclusions can be drawn on the impact of excipient age on observed nitrite levels at present. This will continue to be reviewed as the database collects information on the date of excipient manufacture and date of excipient test. As the database continues to develop, work will continue reviewing trends in the data and to maximize the information that can be shared from this valuable data source.
The evaluation of the available data has shown that some excipients show very little variation in nitrite levels, while others exhibit a much larger spread. However, several of the excipients with higher variability would typically only make a small contribution to the overall formulation and therefore their variability may pose less of a risk. Formulators in the pharmaceutical industry developing new formulations can use the database to choose excipients with low nitrite content, while it can serve as a tool for risk assessments and life-cycle management during the manufacture of established formulations. The database can be used as a key component of the nitrosamine risk assessment process for pharmaceutical products due to the large amount of nitrite data which was previously not available.
The second simulation was performed using a time varying LAI from MODIS which we refer hereafter as MV-V. The daily varying LAI data covers the period from May 1 st to September 30 th. A full description of the SWAMMA simulations is provided by  Roberts et al. (2018). Thus, VegFlux is using the same configuration such as SWAMMA except the vegetation fraction treatment which is invariant in SWAMMA and vary in VegFlux.
2. 3-Data SWAMMA model outputs have been validated using: satellite data from MODerate resolution Imaging Spectroradiometer (MODIS) and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) as well as station data of near surface wind observations from the Fennec field campaign  (Roberts et al. , 2018) , and from the Analyse Multidisciplinaire de la Mousson Africaine -Couplage de l'Atmosphère Tropicale et du Cycle Hydrologique (AMMA-CATCH) observatory in West Africa  (Lebel et al. , 2011). The validation processes is described in  Roberts et al. (2018). Given that the VegFlux simulations are identical in every way to the SWAMMA simulations apart from the source and variability of vegetation type and cover it F o r R e v i e w O n l y 6 was not deemed necessary to repeat the work of  Roberts et al.
Patients Patients with histologically confirmed PDAC, who underwent complete surgical resection at the University Hospital of Besancon between January 2000 and December 2017, were included in a prospective cohort. The database was registered and declared to the National French Commission for bioinformatics data and patient liberty (CNIL; No. of CNIL declaration: 1906173 v 0). The study methodologies conformed to the standards set by the Declaration of Helsinki. A general informed consent was signed by all patients with cancer at the time of their first visit to the Department of Medical Oncology. The experiments were undertaken with the understanding and written consent of each subject. Samples were provided by the regional tumor bank of Franche-Comté (University Hospital of Besancon, France; registration number BB-0033-00024).
Pathological analyses Each slide was digitized after manual optimization with NanoZoomer 2. 0 HT digital slide scanner (Hamamatsu ® , Hamamatsu City, Japan) to generate a whole slide image (WSI) file in ndpi format. Analysis of the immunohistochemical staining was interpreted with QUPATH software (v0. 2. 3)  [20]  by automatic surface detection expressed in percentage of tissue stained for SERPINH1, LRRC15, αSMA, and FAP antibodies. Positive cells were taken into account to evaluate the cell densities (cellsÁmm -2 ) of CD3+. Due to the expression of PTK7 antibody in both tumor and stroma cells, we used a semi-quantitative visual scale to quantify PTK7 in the stroma (0, 1+, 2+, 3+). In case of discrepancies, two pathologists reviewed the slides together and reached a consensus. The mean of the spots for each patient was calculated.
RNAscope RNAscope is a recent RNA in situ hybridization (ISH) method for FFPE tissues, which represents a robust alternative to immunohistochemical techniques in case of the absence of reliable antibodies. As previously described  [21] , three micrometre tissue sections were used for RNA ISH. In situ detection of SALL4 transcripts was performed with RNAscope 2. 5 Assay for Ventana Discovery Ultra system (Advanced Cell Diagnostics, Hayward, CA, USA) using RNAscope 2. 5 vs Reagent kit-RED (ACD #310036). After deparaffinization (ACD #323742) and cell conditioning with Universal Target Retrieval V2 (ACD #323741) slides were maintained at 97 °C for 40 min and treated with protease (RNAscope 2. 5 vs mRNA pretreat 3-Protease, #322218) at 37 °C for 16 min. SALL4 probes (RNAscope 2. 5 vs probe Hs-SALL4, ACD Bio, #505709) were hybridized for 2 h (hardcoded) at 43 °C.
Cell lines MRC5 human lung fibroblast cells (RRID:CVCL_0440) were purchased from RD Biotech (Besancon, France). Colo320 colon cancer cells (RRID:CVCL_1989) were purchased from ATCC (American Type Culture Collection, Manassas, VA, USA). MRC5 and Colo320 cell lines were cultured in Roswell Park Memorial Institute medium (RPMI 1640) supplemented with 10% of heat inactivated fetal calf serum and 1% penicillin plus streptomycin (Gibco, Illkirch, France). Panc-1 pancreatic cancer cells (RRID:CVCL_0480) were kindly provided by IGBMC (Institute of Genetics and of Molecular and Cellular Biology, Illkirch, France). BPC8 cell lines were generated in our laboratory from ascites of patients with pancreatic ductal adenocarcinoma. Panc-1 and BPC8 were cultured in Dulbecco's Modified Eagle Medium (DMEM; Gibco) supplemented with 10% of heatinactivated fetal calf serum and 1% penicillin plus streptomycin (Gibco).
Western blotting Cellular proteins were separated by electrophoresis (SDS/PAGE) in a polyacrylamide gel in presence of sodium dodecylsulfate (SDS) according to their molecular weight, then transferred to a polyvinylidene difluoride film (PVDF) for staining by a specific antibody. Antibodies used were mouse anti-human SALL4 (1/ 100) (sc-101 147; Santa Cruz, Heidelberg, Germany), and mouse anti-actin (1/1 000 000) (A5441; Sigma, Darmstadt, Germany). Blotted proteins were detected and quantified on a bioluminescence imager and BIO-1D advanced software (Vilber-Lourmat, Collégien, France) after blots were incubated with horseradish peroxidase-conjugated appropriate secondary antibody.
Bioinformatic databases The publicly available datasets (open data source) of pancreatic cancer including at least 145 samples of localized adenocarcinoma with transcriptomic (microarray or RNA-sequencing), clinical, and survival data were selected. Samples with a metastatic stage or non-pancreatic tumor (cell lines, normal tissue, or other localizations) were excluded. The development cohort was downloaded from the ICGC (International Cancer Genome Consortium) data portal (dcc. icgc. org, release 26), containing microarray gene expression of 259 treatment-naı¨ve resected PDAC  [2]. The Cancer Genome Atlas (TCGA) project (RNAsequencing: TCGA_PAAD  [22] ) by the Broad Institute platform (https://gdac. broadinstitute. org, release 20160128, already normalized, level 3) and the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) repository (microarray: GSE85916  [5] ), were chosen for validation cohorts.