dataesr/scientific-paragraphs-categorization huggingface dataset:
Duplicate reports were reconciled whenever possible. Postmarketing exposure to patiromer was calculated by exposure in patient-years (PYs). Reporting rates of AEs were then calculated based on the total number of patients exposed in a specific postmarketing period and identified as the number of events per PY and multiplied by 100 to provide the percent events per 100 PYs. PYs are calculated as the number of patients exposed during the analysis period divided by 12 (based on the assumption that the length of patiromer treatment is 30 days). Reporting rate is the number of AEs per 100 PYs and is calculated as total number of AEs divided by PYs × 100. For the global pharmacovigilance database, reporting rates (% of events per 100 PYs) are reported. For the clinical trial program (N = 666)  [10, 11, 15, 16] , the frequencies (% of patients with the event/all patients exposed) are reported.
Results Between January 2016 and September 2019, there were an estimated 45,000 PYs of exposure to patiromer. The total number of ICSRs in the global drug safety and pharmacovigilance database was 17,823, of which 10,970 (62%) were from solicited sources and 5966 (23%) were medically confirmed (reported by a healthcare provider). The total number of AEs in the database was 38,109. Overall, 95% of the patients with AEs were treated in the USA; 9981 (56%) were male, 7664 (43%) were female, and 178 (1%) had unknown gender; 10,298 patients (58%) were aged > 65 years. The source of the AEs was primarily solicited (62%), whereas 38% were unsolicited reports (spontaneous). Of the AE cases, 26% were serious AEs (45% medically confirmed) and 74% were nonserious AEs (30% medically confirmed). Overall, the vast majority of cases had limited information, such as the start and stop date of AEs and patiromer treatment, laboratory parameters, causality, etc.
Deaths During the postmarketing survey period, 1214 deaths (73% medically confirmed) were reported, corresponding to a cumulative annualized mortality rate of 2. 69 per 100 PYs. Of note, no cause was reported for 80% of the deaths. As, in most cases, no further information was obtainable, these deaths could not be medically evaluated by the sponsor. By comparison, in the clinical trial program, 18 deaths were recorded in patients receiving patiromer, resulting in an annualized mortality rate of 5. 81 per 100 PYs. Table  1  lists cases with a reported cause of death ≥ 0. 5% in the global pharmacovigilance database. The majority were in the cardiac disorders class (myocardial infarction, cardiac arrest, cardiac failure, cardiac disorders, congestive cardiac failure). This is consistent with the clinical trial program, in which the adjudicated cause of death in 14 of 18 patients were in the cardiac disorders class (sudden cardiac death, n=10; acute myocardial infarction, n = 4).
Most Common Serious Adverse Events (AEs) Overall, 6% of serious AEs in the global pharmacovigilance database met the criteria of hospitalization. Serious AEs with a reporting rate of ≥ 0. 5% were dialysis (0. 69%; 39. 4% medically confirmed) and pneumonia (0. 58%; 40. 6% medically confirmed). By comparison, there was no report of serious AEs of dialysis in the clinical trial program (patients on dialysis or with a potential need for dialysis were excluded from these clinical trials). Serious AEs of pneumonia were observed in 0. 30% of patients in the clinical trial program. There is no mechanistic causality for these two events, as patiromer is not absorbed, but the treated patient population is predominantly renally impaired. Table  2  shows the most common serious AEs in the clinical trial program (with a reporting rate of ≥ 0.
5%) and corresponding rates in the global pharmacovigilance database. Events of intestinal necrosis and other serious gastrointestinal AEs of special interest per the EMA had a reporting rate of < 0. 05%. These events are included in a biannual analysis for the potential risk of severe gastrointestinal disorders, including intestinal necrosis and perforation. The current analysis concluded that there is no evidence that patiromer caused severe gastrointestinal disorders, such as gastrointestinal ischemia, necrosis, or perforation.
Most Common Nonserious AEs Consistent with the clinical trial program, the most commonly reported clinical nonserious AEs in the global pharmacovigilance data were gastrointestinal (Table  3 ). For the two most common nonserious AEs, constipation and diarrhea, the reporting rates were 6. 90 and 3. 48% in the global pharmacovigilance database and 7. 2 and 4. 8% of patients in the clinical trial program. In addition to lowering serum potassium, patiromer use has been associated with decreases in serum magnesium. Because patiromer exchanges calcium for potassium, increases in serum calcium are of interest. Serum potassium values < 3. 5 mmol/L were reported in 4. 7% of patients in the clinical trial program. "Blood potassium decreased" (the term captured in the global pharmacovigilance database) had a postmarketing reporting rate of 0.
45%. AEs of hypomagnesemia and "blood magnesium decreased" were observed in 5. 3 and 0. 8% of patients in the clinical trial program and 0. 02% and 0. 16%, respectively, in the global pharmacovigilance database. Hypercalcemia was observed in a single patient (0. 2%) in the clinical trial program, and there was no AE of "blood calcium increased. " AEs of hypercalcemia and "blood calcium increased" had a reporting rate of 0. 06 and 0. 09%, respectively, in the global pharmacovigilance database. Hypersensitivity reactions (all mild to moderate) were reported in 0. 3% of patients receiving patiromer in the clinical trial program; reactions included edema of the lips. Hypersensitivity reactions had a reporting rate of 0. 29% (131 events; 44 [33. 5%] medically confirmed); of these, 24   were categorized as serious (five [20.
8%] medically confirmed) in the global pharmacovigilance database. Finally, drug-drug interactions (DDIs) were closely monitored because of the recommended 3-h separation from administration of other oral medications. Most of the reported DDI events occurred during the first 2 years after US marketing approval, and the majority involved consumer inquiries about whether a possible DDI existed. A search of the global pharmacovigilance database was performed for DDIs, which includes reported events of drug interaction, inhibitory drug interaction, and potentiating drug interaction. DDI events had a reporting rate of 0. 1% (48 events, three serious, and 12 medically confirmed).
Discussion This manuscript presents the first global postmarketing safety survey of the sodium-free nonabsorbed potassium binder patiromer (Veltassa ® ) since its approval for the treatment of hyperkalemia in October 2015 in the USA  [1]  and in May 2017 in the EU [2], using the global pharmacovigilance database of a patiromer manufacturer. In the current analysis, with data collected from the beginning of 2016 through September 2019, there were approximately 45,000 PYs of exposure (largely US patients) to this novel potassium binder. The overall mortality rate in this "real-life" nonselected population (2. 69 per 100 PYs) was lower than that observed in the clinical trial program of patiromer (5. 81 per 100 PYs). In the global pharmacovigilance database, 67% of patients were aged > 65 years.
In the clinical trials, 59. 8% of patients were aged ≥ 65 years and 93% of patients had CKD. The demography of patients in the pharmacovigilance database is consistent with the epidemiology of hyperkalemia, in that older patients are affected more frequently. In addition, older patients are more likely to have hypertension, diabetes, and HF requiring RAASi therapy. It seems prudent that all patients with CKD-and most crucially older patientsparticularly those on RAASi medications, have their serum potassium levels monitored regularly. Overall, 80% of deaths in the global pharmacovigilance database had no reported cause and were obtained mostly from solicited sources, which typically are collected on a monthly or more frequent basis. A medical evaluation by the sponsor concerning causality is not possible when very minimal information is provided.
Limitations Several limitations with the global pharmacovigilance database are inherent to postmarketing data, such as limited information on demographics, medical history, posology, laboratory results, start date of drug and event, and health professional assessment of causal relationship. We also acknowledge the potential for under-reporting of AEs in the global pharmacovigilance database, based on the assumptions made in calculating the PYs of exposure. However, the majority of cases (62%) were obtained from solicited programs (which, in the case of the patient-support program, contacted prescribed patients monthly or more often depending on the prescription). Any AE, regardless of causal relationship, that was mentioned during this communication was captured in the pharmacovigilance database.
EXPERIMENTAL SECTION Materials and Methods: Reagents were purchased from Aldrich as reagent grade and used without further purification. Reactions were performed in oven-dried glassware under an argon atmosphere. Analytical thin layer chromatography TLC were performed on silica gel plates and visualized either with a UV lamp (254 nm) or using a staining solution (p-anisaldehyde or KMnO4). Evaporation of the solvents in vacuo was performed at 25-35 °C and 900-10 mbar. Flash chromatography was performed on silica gel (Merck-Kieselgel 60, 230 400 mesh) or on aluminum oxide 90 standardized Merck, Brockmann activity: II-III. Isolated yields refer to spectroscopically and chromatographically pure compounds that were dried under vacuum (0. 1-0. 05 mbar) before analytical characterization. 1  H NMR spectra were recorded on a Bruker AVANCE 400 at 400 MHz and data are reported as follows: chemical shifts in ppm with the solvent peak as internal standard (CDCl3, δ 7.
26 ppm), multiplicity (s = singulet, d = doublet, t = triplet, q = quartet, quint = quintuplet, hex = hexuplet, hept = heptuplet, m = multiplet or overlap of non-equivalent resonances, br = broad). 13 C NMR spectra were recorded on a Bruker AVANCE 400 at 100 MHz and data are reported as follows: chemical shifts in ppm with the solvent peak as internal standard (CDCl3 at 77. 1 ppm). Coupling constants J were measured in Hertz. Infrared (IR) spectra were recorded on a Bruker TENSORTM 27 (IRFT) and wave numbers are indicated in cm -1. Mass spectra with electronic impact (MS-EI) were recorded with a Shimadzu GCMS-QP 2010S (70 eV). High-resolution mass spectrometry (HRMS) was obtained on a LTQ Orbitrap-XL-ETD, Thermo Scientific in the ESI mode. Amorphadiene was synthesized from dihydroartemisinic acid.
, 2018). A carbon source group required a minimum of 6 biochemicals for evaluation. For studies in GF mice, 506 biochemicals, of 787 identified by the Metabolon panel, 64. 3% of the dataset, were curated into carbon source groups (SDF 1. 1-1. 2). For studies in conventional mice, 667 biochemicals of 858, 77. 8% of the dataset, were curated into carbon source groups (SDF 9. 1-9. 2). Mass spectrometry datasets were filtered to remove biochemicals with values <50,000 counts across all samples (<3% of biochemicals). Remaining zero-value data points were assigned a value of 25,000 to support calculation of Log2 fold-change between comparisons. Datasets were Log2 transformed for significance testing of each biochemical by Welch's T test and Benjamini-Hochberg multi-hypothesis correction  (Benjamini, 1995; van den Berg et al.
, 2006). Thresholds for enrichment used a Log2 fold-change of ≥0. 32192809 (1. 25X), and a Log2 foldchange ≤ -0. 32192809 (-1. 25X) for depletion, and per-biochemical adjusted p value ≤0. 05. Biochemicals in pairwise comparisons were ranked by adjusted p value and up to the top 40% of significantly changing biochemicals were used in analyses. The number of enriched and depleted biochemicals per carbon source group, and total number of enriched and depleted biochemicals in datasets were calculated. Carbon source groups with ≥4 enriched or ≥4 depleted biochemicals underwent hypothesis testing by hypergeometric test, followed by Benjamini-Hochberg multi-hypothesis correction  (Benjamini, 1995). An adjusted p value ≤0. 05 for enriched or depleted carbon source groups was considered significant.
Acknowledgments We thank  Andrea Dubois ,  Cameron Friedman ,  Vladimir Yeliseyev ,  Qing Liu ,  Rebecca Krinzman  and  Teri Bowman  for technical support, and  Peter Sewell  for graphic design support. RNA sequencing was performed by the  Harvard Medical School Biopolymers Core. Partners Healthcare 's ERIS team supported the  Massachusetts Host-Microbiome Center 's high performance computing resources. We would also like to thank  Jessica Allegretti ,  Laurent Bouillaut ,  Laurie Comstock  and  Aimee Shen  for critical reading of the manuscript and helpful comments. Funding: This work was supported by the  BWH Precision Medicine Institute ,  Harvard Digestive Diseases Center  grant  P30 DK034854 , and  R01 AI153605  (LB). BP was supported by  T32 HL007627 ; the work of JW was supported by the  Intramural Research Program of the National Library of Medicine, National Institutes of Health.
In addition, according to the PharmGKB database (www. pharmgkb. org), the earch for rs2244613 and rs8192935 CES1 polymorphisms is indicated at Evidence Level 3 (low) for dabigatran, and for the rs776746 CYP3A5 and rs2231142 ABCG2 polymorphisms for apixaban. However, this level of evidence is insufficient to allow implementation of pharmacogenetic testing in clinical practice. This low level of evidence is due to the lack of reproducibility of results between studies  [48]. The DAPHNE clinical study involving a cohort of 350 patients on rivaroxaban and apixaban is currently being conducted by Victoria Rollason's team (University Hospitals of Geneva); it aims to analyze the impact of certain polymorphisms of the CYP3A4, CYP3A5, CYP3A7, and ABCB1 genes, as well as the phenotyping of the proteins encoded by these genes on the pharmacokinetics of these two DOACs  [49].
Nitrogen was used as drying gas as well as nebulizing gas. Parameters were optimized during the development period. The desolvation gas flow was set to 800 L/h and the desolvation temperature was 550 • C. The source temperature was 150 • C. The capillary voltage was tested and set at 1. 50 kV (ES-) and 3. 0 kV (ES+). Cone voltage and collision energy were automatically adjusted to optimize the signal of the 2 most abundant product ions of SARMs and bicalutamide-D 4 using Intellistart software. Transitions, cone voltage and collision energy are resumed in Table  1. MassLynx 4. 1 software was used for identification and quantification.
In-line process monitoring of micromoulding The Micropower 15 in this study is equipped with a range of output channels and sensors for data collection during micromoulding cycles. Available data include injection pressure, injection piston position, and cavity pressure measurement which are captured using The injection pressure measurements were done using the built-in strain gauge sensor (X-Sensors XB 120-1500) on the machine which is located behind the injection piston. The output voltage (0 -10 V) of the sensor was calibrated using a two point calibration according to the values recorded from the machine software (Unilog B8). The position data coming from the servo encoder were also calibrated using an external laser displacement sensor in a two point basis. A PC-based data collection suite incorporating a custom LabVIEW script was created and multiple channels of data were recorded for each individual part produced.
Microreplication assessment Each sample that had 36 microneedles were collected from Set1 and Set2 and inspected under the telecentric inspection apparatus. Two main product criteria were defined for the microneedle replication assessment. The first one is the average microneedle height (hav) calculated for each replica with 36 needles. In a similar fashion, the average tip radius (rav) was also calculated. Fig. 8  shows the results for hav and rav from these measurements. The data in Fig. 8a  show that the increased pressure values used for Set2 resulted in significantly better replicas in terms of needle height. The hav value of 887. 3 μm for Set2 indicates that 97% of the microneedle cavities have been successfully replicated. Thus, it can be clearly stated that the elevated pressure in Set2 reduced the variations in product quality considerably.
Process data analysis Captured process data has been analysed to investigate the trends in product and process quality indicators, namely hav, rav. Ppeak. The data given in Fig. 10  show that Ppeak is an indication of well replicated microneedles where a minimum value of 1275. 3 bar has been recorded for producing a part with hav = 883 μm and rav = 13 μm. It is also important that after this minimum Ppeak value, there is higher variation of Ppeak (SD = 218. 4 bar) for Set2. The higher switch-over and packing pressures for Set2 ensure that the variations that can have an impact on microreplication performance are minimised to negligible values that cannot be detected and/or are insignificant. This is evident from the very low CoV value (0. 4%) of hav (see Table  5 ), which suggests that the process is very repeatable.
Another significant challenge was also addressed in microneedle manufacture by building a bespoke telecentric measurement system for efficient quality assurance. Measurement times as low as 2 minutes for a 6x6 microneedle array are significantly fast and can allow the users to create their own databases for microneedle replication for the assessing the suitability of selected manufacturing processes and detection of process variations. This new quality assessment capability of microneedles also implemented into an in-line quality assurance procedure where the quality criteria of the parts were analysed in conjunction with quality indication features referred as "process.
Acknowledgements This research work was undertaken in the context of  MICROMAN  project (" Process Fingerprint for Zero-defect Net-shape MICROMANufacturing ", http://www. microman. mek. dtu. dk/). MICROMAN is a European Training Network supported by  Horizon 2020 , the  EU Framework Programme for Research and Innovation  (Project ID:  674801 ). This research has also received funding and support from two other  Horizon 2020 projects: HIMALAIA  (Grant agreement No. 766871 ) and  Laser4Fun  (GA no. 675063 ).
Numerical modeling Numerical modeling is nowadays increasingly used in the pharmaceutical industry to study the processes. In the case of compaction, the Finite element method is the most widely used  (Cunningham et al. , 2004; Diarra et al. , 2012; Sinka et al. , 2004; Wu et al. , 2005). It considers the powder as continuous medium which properties are dependent on the relative density. Results published in the literature show good agreement between experimental results and simulation. Finite Elements Modeling Abaqus® software (Dassault Systèmes, Vélizy-Villacoublay, France) was used to undergo numerical simulations of the die compression of press-coated tablets. An axisymmetric modeling was chosen as the geometry, border limits, and loads are all axisymmetric. The dimensions of the core, shell, die and punches were chosen equal to the ones that were used for the experiments.
Drug Titrations and MTT Tests The day prior to drug titrations, Caco-2 cells were split with Accutase ® (catalog # 423201, BioLegend, United States) and seeded at 5,000 cells/well in 96-well plates. Cells in triplicates were then treated for 3 days with nine different concentrations of each of the chemotherapeutics: irinotecan, 5-fluoruracil (5-FU), and oxaliplatin (catalog #I1406-50MG, F6627-1G, and O9512-5MG, Sigma-Aldrich, United States) to determine IC 50 values. On day 3, MTT (methylthiazolyldiphenyl-tetrazolium bromidecatalog #M5655-500MG, Sigma-Aldrich, United States) was added to each well at a final concentration of 0. 5 mg/ml and incubated for 2-4 h at 37 °C. Finally, DMSO was added to a final concentration of 20%, and plates were read at 592 nm. Experiments were repeated at least twice, and data were analyzed and plotted using GraphPad Prism 9.
Data Analysis and Statistics HISAT2 software, DESeq2, and EdgeR package in R were used by Novogene for quality control, mapping, quantification, and differential gene expression analysis. Thresholds for adjusted p-value of ≤0. 05 and fold change (FC) of ≥1. 5 and ≤-1. 5 were applied in the generated lists with differentially expressed genes for further analyses-gene ontology (GO) analysis with BinGO  (Maere et al. , 2005)  in Cytoscape software  (Shannon, 2003)  and gene set enrichment analysis (GSEA) with GSEA software by the Broad Institute  (Subramanian et al. , 2005)  (using standard settings of 1,000 permutations, gene_set as the permutation type, and the H: hallmark gene set from the molecular signature database (with FDR ≤0. 05)). Correlation analysis between differentially expressed genes in the TCGA "COAD" (colon adenocarcinoma, n 271) sample group (vs control tissue, n 41) and 3D bioprinted Caco-2 cells (vs cells grown in 2D) was carried out in GEPIA2  (Tang et al.
, 2019)  following the instructions set by the developers. Data analysis and figure preparation of the results from MTT assays were carried out with GraphPad Prism (v. 8). Datasets from at least 2 separate experiments in technical triplicates were analyzed together by normalizing the values, transforming the drug concentrations to logarithmic scale (log10), and using nonlinear regression to fit the titration curves and calculate IC 50 values. For bar chart figures, the same data from the MTT assays normalized to control (Figure  3A ) and another set of data obtained from 3D bioprints as described previously (Figure  3B ) were used. Student's t-tests were run for every treatment group comparison. The image counting of live/dead cells was carried out manually using ImageJ (NIH, United States).
We first assessed the viability and distribution of cells through the prints. It was found that cells displayed an even spread within the prints, survived for more than four weeks embedded in our constructs, and showed insignificant cell death throughout this follow-up period (Figure  1C  and Supplementary Figure  S1B ). Importantly, monitoring cells for their viability allowed assessment of cell growth and/or migration over time. We observed the formation of larger structures starting from day 3 (data not shown) which became more visible at weeks 1 and 2 (Figures  1C, D ). In parallel, we performed histological analysis which confirmed the cell viability and distribution within the bioprints. The H&E staining and histopathological assessment revealed that the morphology of these larger cell clusters closely resembled small glandular-like structures similar to those observed in situ in CRC.
We found several key patterns of expression, which could be expected and also validate the physiological relevance of the model, namely, cells in 3D bioprints augment transcriptional programs for cell-cell adhesion and hypoxia. Interestingly, GSEA also showed activation of some of the most prominent pathways in CRC-EGFR, K-RAS, and TNFα (via NFkB). Of note, 3D bioprinting appeared to negatively affect cell cycle and replication (E2F targets and progression through G2M checkpoint), and also possibly viability (apoptosis) compared to 2D conditions (Figure  2C -E and Supplementary Figure  S3A-E ). Last, we investigated if the 3D bioprinted model is physiologically relevant by comparing our RNA-seq data to publically available datasets from TCGA. By using the online platform GEPIA2  (Tang et al.