dataesr/scientific-paragraphs-categorization huggingface dataset:
The increasing utilization of human intestinal aspirate studies to facilitate biopharmaceutics characterization  [226]  could provide additional insights by comparing the dissolution rate and nanoparticle formation (especially particle size measurements) between simulated intestinal fluids and in vivo conditions. On the preclinical map, more detailed measurements of diffusion coefficients of pharmaceutical product relevant nanoparticles in intestinal mucus as well as development of imaging protocols, that would allow visualization and quantitation of mucus associated nanoparticles after oral administration, could provide better insight on the absorption process of nanoparticle formulations. Finally, the incorporation of such input data in computational models, coupled with appropriate parameter sensitivity analysis (PSA) to understand identifiability issues, would enable to further develop computational models and validate those against controlled datasets.
Nano constructs engineered with a variety of biocompatible materials have been investigated with varying degrees of success  [236, 248]. Of these materials, pH-dependent ionizable lipids capable of forming lipid nanoparticles (LNPs) incorporating nucleic acid deserve particular mention. LNPs are used to deliver siRNA in the marketed product Patisiran (Onpattro®) and this formulation approach is also used in mRNA Covid-19 vaccines by several companies (NCT04368728, NCT04470427). Both products have shown ≈95% efficacy in Phase 3 trials (Nov 2020) and application for emergency use authorization has been filed to the FDA  [249]. Can these approaches be exploited for oral delivery? 4. 3. 2. 5. What lipids to choose?. The most commonly used LNPs consistently contain a blend of 4 lipids including an ionisable cationic lipid (e.
Combining dissolution results, ideally derived from a biorelevant method which incorporates appropriate physiological variables, with a computational approach is an extended solution to address this limitation  [379] [380] [381] [382] [383] [384] [385] [386]  but is limited in the sense that it delivers a bespoke solution for the compound of interest. To extend this concept of combined in vitro and in silico analysis to a broader compound set would require a multicomponent first principles approach to be applied to all those properties which determine bioperformance for both the drug and the formulation. Such a functional biopharmaceutics profile "2. 0" dataset would need to move beyond static single point measurements for solubility and intrinsic dissolution to a range of measurements which capture the true range of clinical inter-and intra-subject variability.
The same concepts apply to dissolution measurements and in some cases (possibly where active mechanisms predominate) to permeability values. For ionized molecules, more nuanced datasets are required to fully evaluate the potential impact of precipitation processes on dissolution. Studies of supersaturation and precipitation phenomena are widely reported in the literature  [214, 387, 388] , and several reports describe successful predictions of in vivo impact using in vitro data to guide in silico models  [389] [390] [391] [392] [393]. However, the ability to accurately predict solid form, particle size and re-dissolution for precipitated weak bases after gastric transfer remains beyond the current capabilities of the typical biopharmaceutics toolkit. Another factor which requires consideration is that much of our focus remains on physicochemically profiling the drug, yet the conversion to drug product brings with it a number of interactions and potential transformations which are very relevant from a biopharmaceutics performance perspective.
Empirically determining such effects may be necessary to better inform predictive models of product performance. Excipients can also positively impact the surface wettability of drug and thereby improve product bioperformance  [397]. Other opportunities exist to produce a functional biopharmaceutics profile with a richer dataset than that of a traditional general pharmaceutics profile and which critically inform the formulation scientist of the properties which are essential for consistent bioperformance. Placing a focus on the functional biopharmaceutics aspects of the drug to drug product development transition will enable more sophisticated risk assessments to be performed than currently achievable with a conventional approach. Returning to the question posed on the future prospects for the development of a truly holistic model of oral absorption, it is proposed that a more feasible near-term target is to consider combining an end-to-end mindset and enhanced technology toolkit to create a functional biopharmaceutics profile, assembled from a better informed composite array of in vitro, in silico and in vivo data, which reflect the pharmaceutical complexity of the drug product and the physiological variability of the patient.
Acknowledgements. 321 References. 321 Acknowledgements This article is based upon work from  COST Action UNGAP  ( CA16205 ), supported by  COST (European Cooperation in Science and Technology) , funded by the  Horizon   2020 Framework Programme  of the  European Union. The support of  Dagmara Hoc ,  Marcela Wiater ,  Dorota Haznar Garbacz  and  Grzegorz Garbacz  in the edition of a part of the article is kindly acknowledged. The work has been partially financed by  Polish National Centre for Research and Development  ( POIR. 01. 02. 00-00-0011/17 ). Supported by  Ministry of Education, Youth and Sports of the Czech Republic , No. LTC19039  and  LTC18003  ( COST Actions UNGAP and NutRedOx ) and by the  Czech Science Foundation , grant number  18-00132S.
Materials and Methods An international, anonymized survey was conducted from December 2018 to January 2019 via a digital questionnaire in France, Brazil, China, Russia and the USA on participants with self-reported dermatosis (respondents reported that the diagnosis had been confirmed by a physician) or without dermatosis (control-group), using a representative sample of the general adult population aged between 18 and 75 years of age. The survey was conducted by means of local Internet databases of users who agreed to participate in surveys. It complied with local data protection laws and used completely anonymized data. No participant refused to participate. The evaluation was conducted in accordance with the relevant ethical guidelines. In fact, as this study did not involve any patient contact and was completely anonymous, approval from the ethical review board was not necessary.
The SD-VAS is a sensitive, reproducible, reliable and validated measure  31  for the evaluation of sleep quality. Results of SD-VAS data are presented as 95% confidence intervals for the means. 3) Presence of skin unpleasant sensations, such as pain, itch, tingling, burning, or tightness. Three standardized 10-point visual analogue scales (VAS) were used to measure the intensity of itch, pain, and intermediate sensations (tingling, numbness, burning, and tightness); an intensity of 0 meant no symptoms, and 10 indicated the worst possible imaginable sensations. Based on clinical evaluation, male and female patients were divided into four clinical groups, considering the presence or absence of symptoms and the type of symptoms. 1. Absence of skin unpleasant sensation (pruritus, pain, burning, tightness, tingling, numbness) (Intermediate sensations VAS Scoring, Itch VAS scoring and Pain VAS scoring = 0).
Data Analyses Statistical analysis was performed using Stata 7. 0 (Stata corporation, College Station, TX, USA). Data concerning adult individuals between 18 and 75 years of age with at least one dermatosis were extracted from the survey database. Quantitative variables are expressed as the means and standard deviations. Qualitative variables are expressed as frequencies and percentages. Bivariate analyses involving categorical or qualitative variables were carried out with chi-square statistics. Quantitative variables were compared using the Student's test. The presence of a relationship between quantitative features was tested using Pearson's correlation coefficient for parameters consistent with a normal distribution and.
Demographic Characteristics of Patients with Cutaneous Disorders A total of 11,000 individuals participated in the survey, and data from 3834 of them were usable for statistical analysis: 2871 (67. 3%) from the CD group and 863 (32. 7%) from the control-group (Figure  1 ). The recruited patient group with CD [Acne (n=1044); Psoriasis (n=163); Atopic dermatitis (n=254); Contact dermatitis (n=147); Rosacea (n=201); Vitiligo (n=50); Seborrheic dermatitis (n= 195); Scalp disorders (n=216); Photo dermatosis (n=422); Other skin disorders (n=179)] consisted of 1306 males and 1565 females (age 39. 4±14. 5 years, range 18-74 years). Details regarding the patient demographics of the study sample are summarized in Table  1. The 2871 subjects suffering from skin disorders were individualized into 4 groups, considering the presence or absence of symptoms and the type of symptom.
Discussion The data from this international, anonymized internet questionnaire conducted with almost 2871 participants provided information aimed at assessing sleep disturbance in patients with cutaneous disorders. Our study shows that SD is a common problem for patients who have CD, with a high frequency of SD (71. 2% versus 32. 7% in the control population). In our study, the prevalence of SD in the control population was consistent with that reported in the general population by Soldatos et al,  32  who established an SD prevalence of 31. 6% in a study carried out on 35,327 subjects from 10 countries. The severity of SD assessed with the Sleep Disturbance Visual Analogue Scale (SD-VAS) was significantly greater in patients with CD (4. 13418 ± 2. 51 versus 3. 54 ± 2. 32 in control individuals, p: 0.
Background & Aims: Acute decompensation (AD) of cirrhosis may present without acute-on-chronic liver failure (ACLF) (AD-No ACLF), or with ACLF (AD-ACLF), defined by organ failure(s). Herein, we aimed to analyze and characterize the precipitants leading to both of these AD phenotypes. Methods: The multicenter, prospective, observational PREDICT study (NCT03056612) included 1,273 non-electively hospitalized.
patients with AD (No ACLF = 1,071; ACLF = 202). Medical history, clinical data and laboratory data were collected at enrolment and during 90-day follow-up, with particular attention given to the following characteristics of precipitants: induction of organ dysfunction or failure, systemic inflammation, chronology, intensity, and relationship to outcome. Results: Among various clinical events, 4 distinct events were precipitants consistently related to AD: proven bacterial infections, severe alcoholic hepatitis, gastrointestinal bleeding with shock and toxic encephalopathy. Among patients with precipitants in the AD-No ACLF cohort and the AD-ACLF cohort (38% and 71%, respectively), almost all (96% and 97%, respectively) showed proven bacterial infection and severe alcoholic hepatitis, either alone or in combination with other events.
Patients The PREDICT study (ClinicalTrials. gov number, NCT03056612) is a European, investigator-initiated, multicenter, prospective, observational study performed in 48 university hospitals (approved by the respective ethics committees) from 15 countries and promoted by the European Foundation for the Study of Chronic Liver Failure. The design of the study has been reported in detail elsewhere. 2 Briefly, 1,071 cirrhosis patients with AD-No ACLF phenotype and 202 with AD-ACLF phenotype nonelectively hospitalized for treatment were enrolled from March 2017 to July 2018 after providing their informed consent. AD was diagnosed as previously described  2  and ACLF according to the EASL-CLIF criteria. 1, 7 Stratification of patients with the AD-No ACLF phenotype into the AD-pre-ACLF, AD-UDC and AD-SDC sub-phenotypes was performed using previously described criteria 2 and outlined inFig.
Study design The PREDICTstudy 2 was designed to explore the last 90 days prior to hospital admission (especially the last 2 weeks), and the first 3 months after admission (follow-up period), in which the early clinical course of patients was assessed. Pre-specified clinical and standard laboratory data were obtained at enrolment and during follow-up visits. The design of the PREDICT study is described in detail in the supplementary information and elsewhere. 2 entification of precipitants of AD-No ACLF and AD-ACLF In order to identify the precipitants an adjudication committee of the PREDICT study, which included JT, JF, RM and VA, was nominated to elaborate a list of clinical events with the potential to precipitate AD or ACLF, and also the general principles and specific criteria for diagnosis.
05. Overall, the proportion of missing values in the main reported characteristics (demographics, clinical variables, laboratory values, precipitants and clinical outcomes) rounded 1% at most. Only complete clinical blood counts and total cholesterol showed higher proportions of missing data, which were mainly due to common problems with sample availability or with technical laboratory processes that occurred in several site laboratories and can be considered to be completely random. A simple imputation approach was used to impute the missing values for each of the 4 variables mentioned above (neutrophil, lymphocyte and monocyte counts and total cholesterol). SAS PROC MI was used assuming an arbitrary pattern for missing values and adopting a fully conditional specification (FCS) regression method.
Model covariates included age, sex, CLIF-C organ failure score and number of precipitants or presence of bacterial infections or alcoholism or ACLF at inclusion, depending on the   analysis that was to be performed. For each variable, missing values were imputed by computing the median of the values obtained by fitting the model on 100 repetitions generated from the original dataset. Cumulative incidence functions (CIF's) were used to estimate survival curves accounting for liver transplantation as an event 'competing' with mortality, as well as to estimate ACLF development accounting for both mortality and liver transplantation as events "competing" with ACLF development, using common non-parametric methods. The equality of CIFs across groups was evaluated by means of the Gray's test. 15 Statistical analysis was performed using SAS v9.
Acknowledgements The authors are very grateful to the patients, their families and the personnel of the hospitals for making this possible. In addition, a special thank you is dedicated to  Mrs. Yolanda Godoy ,  Dr. Anna Bosch ,  Dr. Josep-Maria Torner ,  Mrs. Cecilia Ducco  and  Montserrat Carreras  for excellent assistance in the accomplishment of the study. We thank  Marites Abans ,  Paul Sauerbruch ,  Gudrun Hack ,  Nadine Köstlmeier ,  Kristin Gehrmann  for technical and administrative assistance.
Prediction of in vivo modulation of SLC transporter activity by ruxolitinib In vivo modulation of transporter activities by ruxolotinib was evaluated from in vitro data using the criteria defined by the US FDA guidance on in vitro drug interaction studies (U. S. Department of Health and Human Services, FDA) Briefly, OAT1, OAT3, OCT2, MATE1 or MATE2-K can be in vivo inhibited by ruxolitinib if: I max, u / IC 50 ≥0. 1 (D) with I max,u = maximum unbound plasma concentration of ruxolitinib and IC 50 = half maximal inhibitory concentration. The equation (D) was also retained for evaluating in vivo inhibition of OCT3 and OAT4. For OATP1B1 and OATP1B3, expressed at the sinusoidal membrane of hepatocytes, an in vivo inhibition can be considered according to the US FDA if: R=1+ ((f u,p × I in,max )/IC 50 ) ≥1.
Comparison with FDA data for in vitro SLC transporter inhibition by ruxolitinib We finally compared data from our present study to those from the FDA report on ruxolitinib (Center for Drug Evaluation and Research 2011), with respect to ruxolitinib-mediated in vitro inhibition of OATs, OATPs and OCTs. As shown in Table  3 , 4/6 transporters, i. e. , OAT3, OATP1B1, OATP1B3 and OCT2, were found to be inhibited by ruxolitinib in the two studies. According to IC 50 values, OCT2 was however almost 10-fold more sensitive to ruxolitinib in our study (based on 4-Di-ASP transport) than in that reported by the FDA (based on 1-methyl-4phenylpyridinium transport). Divergent in vitro effects of ruxolitinib were reported for 2/6 transporters: (i) OAT1, which is rather moderately inhibited by ruxolitinib in our study, was not sensitive to ruxolitinib in the FDA report, knowing however that the maximal tested concentration in the FDA report was 37.
Patients and Data Collection A total 1114 adult patients who received a single kidney transplantation between 1 January 2007 and 31 December 2017 in Lille University Hospital Center, Nephrology and Kidney Transplantation Department, France were retrospectively included in this study. All patients received initial biological induction (antithymoglobulin or anti-CD25 antibodies) and were treated by tacrolimus for more than one year after transplantation. Immunosuppressive protocol consisted in tacrolimus, mycophenolate mofetil (initially 2 g/day, thereafter tapered), and steroids (500 mg at Day 0, 250 mg at Day 1, then 20 mg/day until  Day 7). Steroids were stopped at Day 8 for patients without immunological risk nor delayed graft function. The initial daily dose of tacrolimus (ADVAGRAF ® , Astellas ® , Chuo City, Tokyo, Japan) was 0.
15 mg/kg/day. Then, the dose was adjusted to reach C0 between 10 and 15 ng/mL the first 3 months, 8 and 12 ng/mL within the first year, and later in a range from 5 to 7 ng/mL with tacrolimus daily dose that should not exceed 0. 10 mg/kg/day regardless of CYP3A5 genotype. Liver transplants and patients treated with chronic drugs known to interfere with tacrolimus were excluded. Data were collected from the database CRISTAL (Agence de la Biomédecine, France) and from patient personal records (CNIL agreement number 2214185). General demographic features and possible confounders for allograft failure were extracted from the database. Recipient characteristics included age, gender, weight, height, body mass index (BMI), initial kidney disease, rank of transplantation, duration of dialysis before transplantation, pre transplant immunization (anti class I or class II Human Leucocyte Antigen-HLA), type of dialysis before transplantation, and CYP3A5 genotype.
Univariate models were composed using three effects for each variable: on baseline value, slope (interaction with time) and CYP3A5 genotype. Among these parameters, those which were not significant (p > 0. 20, two-tailed) were removed. If the association on the slope was significant, the corresponding association on baseline value was also considered. Finally, the selected significant variables were further analyzed in a multivariate linear mixed (backward selection procedure, p < 0. 05, two-tailed). The normal distribution of random effect on intercept, random effect on slope, residuals, and homoscedasticity assumption were graphically assessed. All analyses were performed using the 3. 6. 0 version of the R software  [22]  with "nlme" and "survival" packages.
Background: Adverse effects of drugs are often identified after market introduction. Post-marketing pharmacovigilance aims to detect them as early as possible and relies on spontaneous reporting systems collecting suspicious cases. Signal detection tools have been developed to mine these large databases and counts of reports are analysed with disproportionality methods. To address disproportionality method biases, recent methods apply to individual observations taking into account all exposures for the same patient. In particular, the logistic lasso provides an efficient variable selection framework, yet the choice of the regularization parameter is a challenging issue and the lasso variable selection may give inconsistent results. Methods: We propose a new signal detection methodology based on the adaptive lasso.
Background Because the conditions of exposure of an active drug in real life are very different from those of clinical trials, the adverse effects of drugs are often identified once they are introduced on the market. This may be due to a complex interaction with subcategories of population, or to a Monitoring Centre in charge of pharmacovigilance for the World Health Organization. In France, the national pharmacovigilance database is maintained by the National Agency for the Safety of Drugs and Health Products (Agence Nationale de Sécurité du Médicament et des Produits de Santé, ANSM). It contained around 450 000 reports at the end of December 2017. Currently, about 36 000 reports are reported annually. Several automated signal detection tools have been developed to mine these large amounts of data in order to highlight suspicious AE-drug combinations.
These AWs are then incorporated into a lasso logistic regression using the BIC to choose the regularization parameter. We compare both versions of our approach to (i) more classical implementations of the adaptive lasso in high dimension  [23] [24] [25] , (ii) lasso regressions considering cross-validation, BIC or permutations  [26, 27]  for choosing the regularization parameter, (iii) CISL and (iv) the propensity score in high dimension-based approaches that were recently proposed. We conducted an extensive simulation study exploiting real drug exposure data from the French pharmacovigilance database in order to preserve the sparsity of the covariates. We also present an empirical study on the French national database using a large and recently published reference set pertaining to drug-induced liver injuries (DILI)  [28, 29].
Penalization parameter selection We considered three strategies for selecting the penalization parameter: cross-validation, BIC and permutations. One round of cross-validation involves partitioning the dataset into n f subsets, called folds: n f -1 are used as a training set, i. e. the model is estimated on this set, and the remaining fold is used as a validation set where a prediction performance metric (e. g. area under curve or deviance) is calculated. This procedure is repeated so that each fold is used only once as a validation set. An average value of the performance metric and a standard deviation are then calculated over the n f obtained values. In the lasso regression context, cross-validation is performed for each tested λ value. The selected λ according to crossvalidation is the one with the best result in terms of the prediction performance metric selected.
Class-imbalanced subsampling lasso To circumvent the penalization parameter selection issue in lasso regression, Meinshausen and Bühlmann proposed the stability selection algorithm  [21]. Briefly, it consists of perturbing the data by subsampling many times, implementing lasso regression on these subsamples randomly drawn without replacement, and choosing covariates that occur in a large fraction of the resulting selected sets induced by the lasso path of regularization. Ahmed et al. proposed a variation of this method to account for the large imbalance of the outcome that occurs in pharmacovigilance databases: the CISL algorithm  [15]. In CISL, subsamples are drawn following a nonequiprobable sampling scheme with replacement in order to allow a better representation of individuals who experienced the outcome of interest.