dataesr/scientific-paragraphs-categorization huggingface dataset:
1. 2. Biological reagents and equipment All media and supplements for cell culture were purchased from Sigma Aldrich (Saint Louis, MO). LPS from Escherichia coli (K12) was obtained from Invivogen (San Diego, CA). Purified anti-mouse TNF-α antibody clone 1F3F3D4 and biotinylated anti-mouse TNF-α antibody clone XT3/XT22 for ELISA testing were purchased from eBioscience/thermoFischer Scientific (Waltham, MA). Horseradish Peroxidase Avidin (Avidin HRP) was from Jackson (West Grove, PA). For the Flow Cytometry analysis, Fixable Viability Dye (FVD) eFluorTM 660 and Ki-67 monoclonal antibody clone SolA15 were from eBioscience and CD40 APC clone 3/23 was from BD Biosciences (San Jose, CA). Cells were analyzed on a FACScalibur from BD (Franklin Lake, NJ) using CellQuest Software (BD) for data acquisition and FlowJo Software (FlowJo Inc) for data analysis.
METHODS This post hoc analysis pooled data from nine studies which evaluated simeprevir (SMV)-based interferon-free HCV treatment. Three classes of frequently used concomitant medications of interest (CMOIs) were analysed [antihypertensive drugs (AHDs), anxiolytic drugs (AXDs) and lipid-lowering drugs (LLDs)] and categorized as amber or green according to their DDI potential with SMV (green: no DDIs; amber: potential/known PK interactions). Concomitant medications not recommended to be coadministered with SMV were not included. The composite primary endpoint was defined as the frequency of either discontinuation, interruption or dose modification of the CMOI during 12 weeks of SMV treatment.
Study design In a post hoc analysis we pooled data from eight prospective interventional studies and one observational study of SMVbased interferon-free HCV therapy. Studies selected for this analysis were Janssen-sponsored clinical trials evaluating interferon-free combinations of SMV in combination with sofosbuvir (SOF) and/or daclatasvir (DCV) with or without ribavirin (RBV) (Table  1 )  [14] [15] [16] [17] [18] [19] [20] [21] [22]. All studies were conducted in accordance with the 1975 Declaration of Helsinki, approved by independent ethics committees and registered with www. clinicaltrials. gov. Written informed consent was obtained from all patients prior to study initiation. The observational study was conducted at 33 US sites selected to reflect a diverse care setting; monitoring and source data verification were performed.
The CMOIs with known or potential DDIs with SMV were labelled 'amber', and CMOIs with no known or potential DDIs with SMV were labelled 'green'. The grouping into green and amber CMOIs was carried out according to the prescribing information for SMV  [4]  and the Liverpool DDI database (www. hep-druginteractions. org); in the absence of information from either source, expert advice was sought. Medications that are not recommended to be coadministered ('red') were disallowed in all clinical studies with SMV at study entry, and thus this category was not considered in the present analysis.
Study population The overall population was defined as all study patients who had received SMV in combination with other DAAs (±RBV) and were observed for at least 12 weeks. To be included in the DDI analysis, patients also had to be receiving at least one drug from a class of CMOI; patients were required to have started this CMOI before study start or screening, and/or during the screening period. Patients who had not been on a CMOI during screening or at study start but had started a CMOI after initiation of SMV therapy were excluded from the analysis. Two analysis sets were defined: the outcomes cohort, which included data from the screening period and the initial 12 weeks of SMV treatment, and patients receiving both green and amber CMOIs were evaluated twice, separately in each group; and the safety cohort, which included only patient data from the initial 12 weeks of SMV treatment.
Outcome measures The composite primary endpoint described dosing changes of the CMOI as an indicator for DDI management and was defined as either discontinuation (permanent discontinuation or interruption for ≥14 days), interruption (interruption of medication for 2-13 days) or dose modification (any increase or decrease from first recorded total daily dose or subsequent change, including interruptions up to 1 day of SMV treatment). Clinical information on the reasons for discontinuation, interruption or dose modification were not captured in the database, and thus not analysed.
Statistical analyses Statistical comparisons of baseline demographics and disease parameters in green and amber groups were carried out in the safety cohort. Continuous parameters were tested using the Wilcoxon-Mann-Whitney test, and categorical parameters were tested using the chi-square test. Statistical analysis was conducted using the SAS statistical analysis software (SAS Institute Inc. , Cary, N. C. , USA). Representation of patients in both the green and amber groups precluded statistical comparison in the outcomes cohort.
This was confirmed by an additional analysis separating the data from the clinical trials, and the observational study revealing similar low rates of DDI management incidence during the treatment period in both settings. Most patients remained on their original CMOI without dose change or interruption during SMV treatment. In addition, only small differences in CMOI management between green and amber drugs were observed. As green CMOIs are not known to interact with SMV, this indicates that SMV treatment in the presence of amber CMOIs with known or potential PK interactions is manageable with few adaptations. If dose changes or discontinuations were considered necessary, they were to similar degrees implemented during screening and during SMV treatment, except for changes of amber LLDs. Most of these LLDs were statins, which may have been pre-emptively adjusted for potential DDIs with SMV.
Limitations The present study was a post hoc analysis. Patients on green and amber AHDs and AXDs differed slightly in key disease and demographic parameters and in polypharmacy rates, limiting comparisons between groups. Outcomes were not differentiated for patients taking drugs from several drug classes of interest. In the database, there was no information available on whether dose changes or dose interruptions of CMOIs were due to DDIs with HCV treatment. In cases where DDIs may have been the reason for reaching the primary or secondary endpoint, the data did not allow us to identify the interacting drugs (SMV, DCV, SOF or RBV). We did not analyse the potential for DDIs in patients receiving multiple concomitant medications, as the main objective was to elucidate the impact of the short-term anti-HCV treatment.
Conclusions In the present post hoc analysis of pooled data from eight clinical trials and one observational study with interferon-free SMV combinations, coadministration of the evaluated medications with known or potential PK interactions with SMV was manageable. This observation is encouraging and should increase the level of confidence of healthcare providers in handling such DDIs, thereby improving the management of HCV patients. However, healthcare providers should remain vigilant of potential DDIs when administering HCV medication. Our method could serve as a blueprint for the evaluation of the impact of DDIs in other arease. g. with other DAAs, to elucidate the clinical consequences of administering DAAs in a setting with a high rate of comorbidities and concomitant medication. With this method, it would be possible to utilize clinical study outcomes data in retrospect, in the absence of prospectively collected PK data.
7%) Naïve, no cirrhosis, treated with 14 (OPTIMIST-1) SMV + SOF NCT02268864 Interventional 106 (12. 1%) Naïve, treated with SMV + DCV in 18 (COMMIT) GT 1b NCT02114151 Interventional 103 (11. 8%) DAA-naïve, with cirrhosis, treated with 15 (OPTIMIST-2) SMV + SOF NCT02165189 Interventional 46 (5. 3%) Liver transplant, treated with SMV + 20 (GALAXY) SOF ± RBV NCT02278419 Interventional 43 (4. 9%) Naïve or experienced Egyptians, GT 4, 16 (OSIRIS) treated with SMV + SOF NCT02262728 Interventional 40 (4. 6%) Cirrhosis, decompensated liver disease, 22 (IMPACT) GT 1 and 4, treated with SMV + SOF + DCV NCT02250807 Interventional 40 (4. 6%) Naïve or experienced, GT 4, treated 17 (PLUTO) with SMV + SOF NCT01938625 Interventional 35 (4. 0%) (SATURN) Liver transplant, GT 1b, treated with SMV + DCV + RBV 21 DAA, direct-acting antiviral agents; DCV, daclatasvir; GT, HCV genotype; RBV, ribavirin; SMV, simeprevir; SOF, sofosbuvir Clinical impact of PK interactions between SMV and concomitant medications Br J Clin Pharmacol (2018) 84 961-971 963 retrospectively.
1% (1 mg/mL) CsA (CsA CE; Ikervis; Santen SAS, Evry, France) was approved by the European Medicines Agency for the treatment of severe keratitis in adults with DED that has not improved despite treatment with tear substitutes. 2] [13] [14]  The efficacy and safety of CsA CE were assessed in two double-masked, randomised, parallel-group, vehicle-controlled phase III studies: SICCANOVE, in patients with moderate-to-severe DED; and SANSIKA, in patients with severe DED. ] [17]  Pooled data from the SICCANOVE and SANSIKA studies were analysed to better understand the magnitude of the treatment effect of CsA.
MeThods study design and participants Data for this pooled analysis were obtained from two randomised, 6-month phase III clinical studies (EudraCT numbers: 2007-000029-23 (SICCANOVE), 2011-000160-97 (SANSIKA)) that evaluated the efficacy and safety of CsA CE in patients with moderate-to-severe DED. 15 16  Both studies were conducted in accordance with the principles of Good Clinical Practice and with the ethical principles set out in the Declaration of Helsinki. All enrolled patients provided written informed consent. Efficacy and safety methods and results from each study have been previously described  15 16  ; the design of both studies is outlined in online supplementary figure  1  and briefly described below. The SICCANOVE and SANSIKA studies were similar in design, allowing the data to be pooled for analysis.
CFS measurements (changes from baseline to month 6) were analysed by age, sex, menopausal status, SS status and disease duration in a subset of patients (n=629) who had CFS values at baseline and at month 6. The model was adjusted for treatment, study, visit and treatment by study. HLA-DR data were analysed after logarithmic transformation using an analysis of covariance model with 'treatment' and 'pooled country' as fixed factors and baseline score as a covariate. Because the data distribution for HLA-DR expression was found to be log-normal, median, rather than mean, values were reported as a measure of location.
In general, a software platform is an operating environment that is used to write (in the case of coding a model), compile (in the case of building a model in software or platform), and run applications. In the case of a PBPK model, the platform includes three key components: computational framework, physiological framework of the system, and drug properties. The computational component includes the program code, model structure, mathematical equations, as well as a runtime engine for executing applications. The physiological framework comprises systemdependent parameters that describe the physiology of human or preclinical species. These system parameters are populationspecific and account for population variability and correlation between parameters. A PBPK platform may also contain a database of virtual populations such as healthy volunteers of different ethnicities, or populations with organ impairment.
PLATFORM QUALIFICATION A PBPK platform is an integrated software environment that allows building and running PBPK models that may or may not provide compound or population-specific databases. From a software perspective, a platform includes various components such as graphical user interface (GUI), data structures, collections of various models, computational engine, as well as interfaces for presenting the simulation results. The PBPK models within a platform are developed to handle specific tasks based on certain assumptions. For example, a minimal PBPK model is developed to predict the drug plasma concentration and some of the relevant PK parameters such as area under the curve (AUC), maximum concentration (C max ), time to achieve maximum concentration (T max ), and clearance values. As long as the general model assumptions are valid and the correct drug and system parameters are used, the model can be used to simulate any drug regardless of how the drug is eliminated or what enzymes are involved.
Computational framework Design qualification (DQ). DQ is the documented collection of activities that define the processes of design, implementation, functional, and operational specifications and intended purpose of an instrument or a software platform. These processes are often part of a comprehensive quality assurance (QA) framework. DQ may be performed by the instrument manufacturer or the user. The supplier is generally responsible for robust design and maintaining documentation describing how the platform is developed and data in databases are analyzed and populated. Nonetheless, the user should ensure that the platform and relevant databases are suitable for their intended application and may evaluate whether the supplier has adopted a quality system that provides reliable software and databases.
As an example, the QA system used to develop the Simcyp platform is described in Jamei et al. 7  Installation qualification. When a user receives a software package and installs it onto their computer, it may have different settings from the machine used to develop the software. Therefore, it is necessary to ensure correct installation of the platform and confirm that results can be robustly reproduced on the users' computers. To this end, specific test simulations should be carried out to verify that the platform uses the same input file and generates similar output files as provided by the platform producer. These procedures ensure that the results are reproducible and no unintended problems are introduced while installing the platform (more information in Supplementary Text 1).
18 mong special populations, the pediatric population has been reasonably well studied, although primarily with CYP3A substrates. 19 Potential strategies to develop, qualify, and verify other virtual special populations are described below. Population qualification in PBPK models involves development of the system-dependent parameters, followed by prospective prediction of PK in the population of interest (verification). The development of any virtual population in PBPK platforms involves evaluating the ability of the platform to construct virtual individuals with anatomical, physiological, genetic, and biological values similar to the observed values, i. e. , does a specific population generate age, body weight, body height, and tissue volumes similar to the observed data? In the case of establishing a "healthy volunteer" population, population-related data are generally assembled from public health databases such as NHANES (USA), NISRA (UK), and Statistics Bureau (Japan).
DRUG MODEL VERIFICATION Development and Verification of drug models PBPK model development is an iterative process that may involve multiple cycles of "predict, learn, confirm. " Usually, a base model is first developed using experimentally determined or in silico predicted physicochemical and in vitro drug absorption, distribution, metabolism, and excretion (ADME) parameters. The predictions from the base model are then compared with the observed clinical PK data (e. g. , PK from single ascending dose (SAD) or multiple ascending dose (MAD) studies) where a selection of model parameters may be adjusted to improve the drug model predictive performance based on sound physiological and scientific evidence. The predictive performance of the refined model will then be confirmed by comparing the prediction with the additional clinical PK data such as results from a DDI study that were not used in previous steps.
An exception would be if multiple mechanisms affect the same enzyme or transporter, or enzyme-transporter interplay. For example, if a drug inhibits and induces CYP3A4, the model needs to be verified for both mechanisms prior to declaring that the model is verified for CYP3A-related applications. It is important to distinguish the studies used for initial model development and refinement (the training dataset) (see section on approaches to PBPK models), from the dataset from studies used for model verification. The design of a clinical study and the use of data to support model verification should be based on the purpose of the application. If a substrate PBPK model is to be applied for DDI predictions with moderate and/or weak inhibitors, it is preferable to evaluate the predictive performance against the observed DDI with a strong inhibitor of the same enzyme.
For both model refinement and verification, C max , T max , CL po (and CL and V if i. v. data are available), and AUC are some key parameters that may be used for comparison between predicted and observed values. Visual inspection of overlays of predicted and observed PK profiles is also performed. When evaluating the accuracy and acceptability of predictions, a commonly applied criteria is for values to be within 2-fold of the observed values. However, results from one controlled clinical study may not be representative of the larger population, especially for drugs that exhibit high variability in PK or if the sample size was small in such studies. As a result, the 2-fold criterion may be unreasonable for such drugs or studies. Instead, Abduljalil et al. 29  proposed to evaluate the success of model predictions taking into account study sample size and the observed variance of the parameter of interest.
In this case, the model loses mechanistic meaning and applicability and is said to be "unidentifiable. " As a result, extrapolation to populations outside the studied conditions is unjustified or may lead to false conclusions. 17, 31 arious mathematical identifiability analysis approaches have been previously described in the literature. 32, 33 roposed approaches to deal with identifiability issues when fitting a PBPK model include measuring some of the unknown physiological parameter values, if possible, reduction in the number of parameters (by grouping several unidentifiable parameters into a single identifiable parameter), redefining parameters (reparameterization), or generating data that could be used in calibration with a different in vivo dataset. 31, 32, 34, 35 Several statistical approaches can also be used to declare a PBPK model as identifiable.
The parameter(s) to be refined in a middle-out PBPK model are usually uncertain due to experimental challenges, measurement with low confidence, or lack of data (such as kinetic parameters and ontogeny profiles of transporters, etc. ) and can be sourced from a semimechanistic PopPK model. The model predictive performance should be assessed using external verification datasets from independent clinical studies to verify the model. After verification, the refined model can be used to address clinically relevant questions, such as requesting a waiver for dedicated clinical trials, or to extrapolate PK from adults and younger children to neonates and infants to examine the existing dosing algorithm in adults. 39, 40.
Examples of PBPK submissions to regulatory health authorities Examples of PBPK models being used to support submissions to regulatory agencies over the past few years have been summarized (Tables  2, 3 ). As highlighted in previous publications, the majority (71%) of these PBPK-based submissions were related to DDI cases, while non-DDI cases represent a smaller (29%) portion of the submissions. These case studies cover a broad range of themes, including but not limited to: DDIs, pH-dependent interaction with proton pump inhibitors (PPIs), and special populations across various phases of drug development. Each example is presented with a brief description of the background, key regulatory questions being addressed, and a summary of the qualification dataset and regulatory dialog. Both successful and unsuccessful case studies from a sponsor or regulatory acceptance perspectives are presented in order to note trends across the regulatory community.
The examples were compiled via an informal survey among the Simcyp Consortium members. Participants were asked to report cases where PBPK models were used to address key questions regarding DDI-and non-DDI-related issues. Case studies in which PBPK analyses were used in the submission were described, with information on the modeling approach (e. g. , bottom up / middle out / top down) and use of clinical data for model qualification. Additionally, participants were asked to report any feedback they received from the FDA and/or the EMA in response to the PBPK submission. There were 20 examples provided for DDI-related issues, including 13 for drugs already on the market, seven during new drug application submissions, or at the end of phase II development. The key DDI issues for all compounds were considered to have a medium to high impact on the submission.
Model assumptions Description and discussion/ justification of the main assumptions in the drug model Technical A summary of the main assumptions, such as: ADME related assumptions, for example, Perfusion limited or permeability limited distribution is needed or assumed; any assumptions on transporters involved; assumptions on the involved metabolizing enzymes and/or percent of enzyme contributions/ fraction metabolized, etc. Population related assumptions, for example, assumed changes in any system-specific parameters, assumed enzyme ontogeny profiles when scaling to children (especially if the ontogeny profile is not included in the built-in database)   4  Continued on next page related information on CYP expression and turnover in PBPK platforms, and the cited literature, it is scientifically justifiable to apply PBPK modeling and simulation for the evaluation of CYP (and arguably UGT) induction by drugs as victims or perpetrators.
ACKNOWLEDGMENTS We are grateful for review and constructive input from  Professor Amin Rostami-Hodjegan ,  Professor of Systems Pharmacology  at the  University of Manchester, UK , and Senior Vice President of Research & Development and Chief Scientific Officer at Certara. We also thank  Dr. Klaus Jensen  for participating in early discussions,  Dr. Allison Kitten  for assistance in formatting the article, and  Miss Eleanor Savill  and  Miss Rosalie Bower  for their assistance with collecting the references and preparation of the article.
enzyme to elimination of the drug. IVIVE and scaling factorsDrug as perpetrator Similar to those presented above for 'Drug as perpetrator' of enzyme inhibition. Area of PBPK Specific application purpose PK characterization Knowledge gaps DDI involving enzymes Drug as victim Abundance data for non- CYP mechanisms Sufficient clinical datasets for qualification of non-CYP3A mechanisms Drug as perpetrator Sufficient clinical datasets for qualification of non-CYP3A mechanisms IVIVE for non-CYP mechanisms DDI involving Drug as victim Similar to those presented above for 'Drug as victim' transporters of enzyme inhibition. Sufficient clinical datasets for qualification Extrapolation from Pediatrics, Elderly, Mechanistic understanding of PK in base population Knowledge of any additional healthy to other Obese may be challenged by lack of i.