dataesr/scientific-paragraphs-categorization huggingface dataset:
Drug release studies For the proper treatment of cancer disease, tumor-targeted drug delivery is required with minimum or no release of drug before the targeted site. Otherwise, the first pass effect can reduce the efficacy of the drug and leads to failure of treatment. Therefore, sustained and targeted delivery of hydrophobic drugs is required with maximum efficacy and minimum toxicity. The diffusion bag technique was used to achieve invitro drug release, and data were collected for all the prepared MTX-LSMCSNPs formulations. The release studies were carried out both at normal simulated conditions (pH 1. 2 and pH 7. 4) and pH linked to the tumor microenvironment (pH 5. 5). At pH 1. 2, all formulations showed drug release within the range of 8 to 14 % and results were insignificant between all formulation (F1, F2 and F3).
At pH 5. 5, cumulative % release was 58-69 %, while at pH 7. 4, it was recorded around 47-58 % up to 24 h. The drug released quickly at pH 5. 5, due to protonation of carboxyl and amino groups present on LSM-CS surface enhanced the swelling capacity of both polymers. Faster release at pH 5. 5 was also due to repulsion happened between similar charges. Whereas at pH 7. 4 release  was slow as the swelling index was low due to deficient surface charges  (Najafipour et al. , 2020). The in vitro release data indicated that the MTX-LSMCSNPs formulation showed sustained drug release with a steady rise as shown in Fig. 7. Overall, the F3 formulation had superior and sustained drug release than F1 and F2 due to a higher concentration of LSM than chitosan. On the other hand, in the F2 formulation, the percentage of drug release was reduced due to higher concentration of chitosan than LSM.
In-vitro drug release kinetics Five different model-dependent kinetic approaches were employed to analyze the drug release kinetics from the of MTX-LSMCSNPs. DDsolver software was used to study that the drug release kinetic behavior from the prepared formulations  (Mircioiu et al. , 2019). The findings are summarized in Table  5. All of the formulations of MTX-LSMCSNPs (F1, F2, and F3) followed the Higuchi model at both pH 5. 5 and pH 7. 4, with the highest R 2 values as compared to all other models. R 2 values were near to one that represents these MTX-LSMCSNPs formulations following the Higuchi model approach. Higuchi model approach is chiefly used for studying the drug release kinetic from the nanoparticles formulations. The Higuchi model is generally linked to three types of hypothesis.
Stability studies The size and encapsulation efficiency of the MTX-LSMCSNPs were measured at room temperature for one month to ensure stability. Data were first gathered daily, then on a weekly basis (Fig. 8 ). During the study period, no growth and no significant variation in the size of nanoparticles was seen or the encapsulation effectiveness of the formulation (F3), indicating the stability of the encapsulation layer for MTX-LSMCSNPs.
Examples of such tests include pH and osmolality"  [7]. However, other guidelines devoted to stability studies such as ICH Q1A or ICH Q1E or NHS guidelines do not mention osmolality as a potential stability criteria  [8] [9] [10] [11] [12]. Many authors use this analytical technique to evaluate the stability of solutions mainly for eye drops. In the Stabilis database, there are 29 monographs of eye drop solutions including 52 publications. Among these 52 publications, 29 use osmolality as a stability criterion i. e. a percentage of 55. 8%. However, stability studies of injectable solutions with 476 monographs and 2095 publications, the partially evaluation on 37 publications give a 1. 77% of osmolality measurement as stability criterion. In the absence of consensus on the use of this criterion to assess stability in the recommendations and in view of the use of this criterion in numerous publications, our objective was to assess the relevance of the measurement of osmolality as a criterion of stability of a solution.
Acknowledgments: Thank you to  Franck Blaise ,  Nathalie Sobalak  and  Hubert Zenier  for their help and their involvement throughout this stability and thank you to  Jacques Kuhnlé  for reading through it all and making corrections. Research funding: None declared. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. Competing interests: Authors state no conflict of interest. Presentation: Presented as a poster a European Congress of Oncology Pharmacy, 2022, Hambourg (Germany). Informed consent: Informed consent was obtained from all individuals included in this study. Ethical approval: The local Institutional Review Board deemed the study exempt from review.
In the 19 F NMR spectra, the amines were characterised by single, welldefined resonances at δ F -135. 2 ppm for 1-H, -135. 0 ppm for 2-H, and -135. 4 ppm for 4-H, with corresponding integrations of 1F, 2F, and 1F. The pertaining | 1 J C,F | coupling constants, determined from the 13 C{ 1 H} NMR spectra, are 236. 5, 238. 5, and 241. 5 Hz, respectively. These latter values are typically in the range found for other C arene -F bonds  (Skeel et al. , 2020). The 19 F NMR spectrum for 3-H exhibits the expected resonances at -159. 0 (o-F), -165. 4 (m-F), and -173. 7 (p-F) ppm. The NMR data for 2-H were also recorded in thf-d 8 ; they confirmed that the coupling constants, most notably the J C,F ones, are, within the limits of the detection method, not affected by the nature of the solvent, and that the 19 F NMR spectrum is identical to that in benzene-d 6.
The equimolar reaction between the fluoroarylamines and nBuLi in petroleum ether at room temperature afforded the corresponding solvent-free lithiated N- (2-fluorophenyl)- trimethylsilylamide [LiN(SiMe 3 )(2-C 6 H 4 F)] (1-Li), N-(2,6-difluorophenyl)-trimethylsilylamide [LiN(SiMe 3 ) (2,6-C 6 H 3 F 2 )] (2-Li), N-(pentafluorophenyl)-trimethylsilylamide [LiN(SiMe 3 )(C 6 F 5 )] (3-Li), and N-(2-fluorophenyl)-1,1- dimethylsilylamide [LiN(SiMe 2 H)(2-C 6 H 4 F)] (4-Li) in good to high isolated yields (71-90%). The four lithiated amides were obtained as colourless powders. They are sparingly soluble in hydrocarbons, but they dissolve readily in Et 2 O and in thf at room temperature; they decompose rapidly in chlorinated solvents. Due to the limited solubility of all four complexes in aromatic hydrocarbons, their NMR data were recorded in thf-d 8.
Study design This study was conducted from February 2021 to September 2021 in collaboration with several AIDES sites. It was a prospective multicenter study conducted in Auvergne-Rhone-Alpes, France, with six centers (Grenoble, Clermont-Ferrand, Lyon, Annemasse, Annecy, and Bourg-en-Bresse). Toxicological analyses were performed at the Pharmacology, Pharmacogenetics, and Toxicology Laboratory, Grenoble Alpes University Hospital, France. The inclusion criteria were: 3-MMC users over the age of 18 years in contact with the AIDES association and providing written non-opposition to the use of their data. A flowchart of the study is presented in Fig. 1. The study received ethics approval from the HDH (Health Data Hub) under number F20210126201110. 46 questionnaire and 45 samples from 33 users included.
The flow rate of the carrier gas (helium) was maintained at 1 mL/min in constant-flow mode. The gas chromatograph interface temperature was held at 315 °C. Electron impact ionization was performed at 70 eV, with an ion source temperature of 230 °C and the collection of mass spectra from 40 to 600 m/z. Data were analyzed with Mass-Hunter (v10. 1, Agilent Technologies), including the Quantitative Analysis and Unknowns Analysis modules in particular.
Key elements of each of the case examples are summarized in Table  1. We also illustrate example RWD applications to inform decisions throughout product development stages with clinical pharmacology-related applications highlighted in Figure  1. Finally, we discuss strengths, limitations, and key data elements for commonly used sources of RWD (e. g. , EHRs, medical and pharmacy claims, and patient registries) to guide the selection of appropriate RWD to investigate clinical pharmacology questions in drug development and approval.
To have a realistic DDI risk assessment of the investigational molecule and inform clinical pharmacology strategy in clinical trial design, RWD from IBM Marketscan (a large, administrative US claims database) were used to derive the frequency of prescription claims for drugs that are considered strong/moderate CYP3A4 inhibitors/inducers and sensitive CYP3A4 substrates in the target patient population, and also derive their length of treatment to determine the chronicity of polypharmacy. It showed that, in patients with psoriasis, the majority of prescriptions of sensitive CYP3A4 substrates included corticosteroids, contraceptives, statins, and antibiotics, of which > 50% were used for > 90 days. Thus, it indicated a risk for the investigational molecule as a perpetrator with the potential to limit the exposure and effectiveness of some chronically used CYP3A4 sensitive substrates (e.
Pediatric registry ImproveCareNow Indicated that the majority of pediatric patients from the RWD database were treated with the equivalent adult labeled dose of vedolizumab and efficacy was similar or slightly better in the pediatric study cohort compared with that observed in adult clinical trials   19  Then, they identified 3,670 drug pairs taken by patients in the real world based on analysis of a clinical repository containing over 800,000 patients. Finally, based on rigorous statistical evaluation, five new drugdrug pairs were identified which had increased risk of myopathy, thus demonstrating clinically relevant DDIs through CYP enzymes. Authors conclude that similar automated search algorithm may be beneficial in identifying additional clinically significant interactions of the FDA approved drug by leveraging published data and large RWD clinical databases.
This can be achieved by either conducting a dedicated PK study in patients with organ impairment or allowing the enrollment of patients with organ impairment in phase II and phase III studies. For example, to assess the feasibility of conducting dedicated organ impairment studies in patients diagnosed with diffuse large B-cell lymphoma (DLBCL), an exploratory analysis was performed using RWD on previously untreated patients with DLBCL (n = 1,341) extracted from the Flatiron Health database. 28 A relatively low percentage of patients with DLBCL with moderate or severe hepatic impairment (< 6%) or with severe renal impairment (< 6%) were found in the database, indicating a very challenging enrollment for a dedicated organ impairment study. In addition, the analysis showed that the study population in phase II/III trials cover the majority (> 90%) of the previously untreated patients with DLBCL in the Flatiron RWD, and a dedicated hepatic/renal impairment study would be of limited added value.
e. , shorter real-world time to treatment discontinuation and overall survival) than patients with normal organ function. This information may be used to evaluate the need of dose adjustment in the organ impairment population. RWD can also be an effective tool in assessing the time course of organ impairment progression, especially in certain rare diseases, and subsequently guiding clinicians on the timing of dose adjustment with respect to organ impairment. Sybing et al. 30  used RWD to characterize the time course of glomerular hyperfiltration (defined as higher-than-normal renal filtration rate) in pediatric and adult patients with sickle cell disease (SCD), which is a driver of renal impairment in later years. The onset and peak of glomerular hyperfiltration, and subsequent decline in renal function in patients with SCD, were characterized using RWD from the Optum EHR database.
33 The results indicated that the majority of pediatric patients from the RWD database were treated with doses equivalent to the adult labeled dose of vedolizumab, and the efficacy was similar or slightly better in pediatric cohorts compared with that observed in adult pivotal clinical trials. The key limitation of this study was related to the dosing data. This is because vedolizumab doses (off-label use) in pediatric patients are not standardized in real-world settings (e. g. , some doses are fixed while others are body weight-based) and dose disparity as dose can be confounded by disease severity (i. e. , more severe patients are likely to be given higher doses as compared with less severe patients), both of which may introduce bias in the analysis. RWD were also used to support dosing in younger pediatric populations where dose recommendations are not yet available.
For instance, lacosamide, an anti-epileptic drug, is approved for the treatment of focal seizures in children ≥ 4 years of age and adults. Researchers used real-world therapeutic drug monitoring data from 315 pediatric patients (> 1 month to < 18 years of age) who received lacosamide to build a population PK model with allometric scaling of body weight and covariate analysis (age included). 34 he model was used to simulate lacosamide exposure for ageassociated doses to match the exposure in children ≥ 4 years of age with the weight-based dosing recommendations provided by the FDA. Using this approach, the authors provided dose recommendations for children aged 1 month to < 4 years old. Potential limitations of this study included imbalance in patient representation across age groups, co-medications, and a higher variability in drug dosing and follow-up timing in the RWD.
g. , inform virtual population generation in QSP modeling). Notably, RWD/RWE provide insights into disease progression by capturing information on real-world patients in the real-world setting. Unlike clinical trials, which provide a snapshot of a patient's journey, RWD with long follow-up can better capture the natural history of a disease and/or disease progression as well as potential risk factors which may influence disease progression. For instance, researchers developing models to complement drug development efforts for Alzheimer's disease used RWD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database alone or together with placebo data from interventional clinical trials, to predict disease progression. 36, 37 By predicting individual trajectories of disease progression in the absence of treatment, the model can reliably assess drug effects for molecules in development and can be leveraged for study design of future clinical trials.
Use of an RWD patient cohort allowed for a larger sample size, more diverse patient population, and longer follow-up compared with a clinical trial. Combining clinical trial data with RWD allowed a richer dataset for model building, and conducting external validation with clinical trial data also helped assess predictive performance of the model. 37 RWD may be particularly valuable to characterizing disease progression in rare diseases, where clinical trial size is limited by the small patient pool. Boucher et al. 38  used RWD from the Transthyretin Amyloidosis Outcomes Survey (THAOS) to investigate the natural history of disease progression as well as treatment effect in patients diagnosed with hereditary transthyretin amyloid polyneuropathy. Similarly, using an existing Duchenne muscular dystrophy (DMD) natural history database, researchers built independent longitudinal models for the North Star Ambulatory Assessment total score for ambulatory boys and for the forced vital capacity percent predicted value for non-ambulatory boys to characterize disease progression of DMD and identified risk factors having a significant effect on outcomes in boys diagnosed with this disease.
39 These models informed the quantitative understanding of disease progression in rare diseases, and simulation results from the models could then contribute to the study design of future clinical trials to accelerate clinical drug development. Besides disease progression models, RWD can be used to enrich and enable other types of models to inform drug development. For example, a QSP model for Gaucher disease type 1 (GD1) was built with the intention to predict treatment response within the heterogeneous GD1 patient population. 40 Researchers in this study were able to use data from a variety of sources, including RWD from the International Collaborative Gaucher Group (ICGG) Gaucher Registry. This registry consists of more than 6,000 patients with Gaucher disease across more than 60 countries, allowing for integration and evaluation of response from more diverse and representative patient populations relative to those enrolled in clinical trial settings.
IDENTIFY PROGNOSTIC AND PREDICTIVE BIOMARKERS/ FACTORS RWD can be leveraged to identify factors for disease prognosis or to elucidate increased treatment benefit via modeling approaches, which can inform patient subpopulation classification of either high-medical need or high-benefit potential. To enhance clinical utility, data from clinical studies are often combined with data from real-world sources to either establish or validate prediction models. For example, Julian et al. 44  utilized a retrospective observational patient cohort with advanced non-small cell lung cancer (NSCLC) from the Flatiron Health database to develop a novel prognostic model to identify disease prognostic factors for OS. A Cox proportional hazards survival model was built with RWD from 4,049 patients with advanced NSCLC on selected second line anti-PD1/PDL1 monotherapy (atezolizumab, nivolumab, or pembrolizumab).
Two hundred five sarilumab users were then identified from the American College of Rheumatology (ACR)'s Rheumatology Informatics System for Effectiveness (RISE) data from 2017 to 2021 based on prespecified inclusion/exclusion criteria. Logistic regression indicated that better sarilumab responses were demonstrated in rule-positive patients compared with those in rule-negative patients in the real-world setting. This RWD application illustrated the use of RWE to understand disease and treatment response and identify/confirm treatment benefit differentiation factors.
In this case, RWE was generated using retrospective analysis of the Scientific Registry of Transplant Recipients (SRTR) database (the most comprehensive database of transplant recipients in the United States). Analyses indicated that lung transplant recipients who received a combination of TAC with mycophenolate mofetil or azathioprine at hospital discharge had high survival rates at 1-year post-transplant, supporting the use of TAC in these combinations as maintenance immunosuppression in adult lung transplant recipients. This RWE, along with evidence from pivotal RCTs of TAC and a meta-analysis report of three RCTs in patients with a lung transplant with TAC-based treatment, provided totality of effectiveness and safety evidence that led to label expansion of TAC to include lung transplantation.
50 ased on RWD, men with HR+/HER2-mBC were shown to benefit from palbociclib plus endocrine therapy (ET). 51, 52 albociclib treatment in men with mBC was evaluated using three independent RWD sources: IQVIA Insurance database (pharmacy and medical claims, n = 1,139), Flatiron Health breast cancer database (EHRs, n = 59), and a global safety database. RWE indicated that men with mBC benefit from palbociclib plus ET, with a safety profile consistent with previous observations in women with mBC. In addition to RWE, similar palbociclib exposures were demonstrated in men and women using a population PK analysis. 53 herefore, RWE together with previous pivotal clinical trials data and a well-established benefit/risk profile of palbociclib led to the palbociclib label expansion to include men with HR+/HER2-mBC in the United States.
56 Because of these logistical and ethical challenges, external control arms based on external data (e. g. , RWD) may be considered as an alternative. An external control is based, at least in part, on external data, which refers to any relevant source of clinical data not from a concurrently randomized control group within the same study. 57 The external control may be selected patient cohorts from one or more external data sources and also modified using statistical methodologies, and it is also referred to as the synthetic control. 58 xternal control arms based on RWD have been used to support regulatory filings. 59 Avelumab was approved for the treatment of metastatic Merkel cell carcinoma, a rare skin cancer. Data from an EHR database were used to create an external control arm and were considered in the FDA review as an exploratory component to "further characterize the risk/benefit profile of avelumab.
"  60 nother relevant example is blinatumomab, which is indicated for Philadelphia chromosome-negative relapsed or refractory Bcell precursor acute lymphoblastic leukemia (ALL). Retrospective ALL patient data were used to construct an RWD-based external control using propensity matching and compared with RCT data from a single-arm efficacy study conducted for blinatumomab. 61 arious challenges (e. g. , non-contemporaneous historical control and difference in follow-up duration), were identified as limiting factors for a reasonable comparison. The review concluded, "the propensity score analysis was not sufficient to determine the estimate of the benefit of blinatumomab. " Both reviews provided insights into how external controls may be used to support regulatory filings and highlighted data quality and analytical approaches which may maximize the utility and acceptance of external controls by health authorities.
CONSIDERATIONS FOR SELECTING COMMON SOURCES OF RWD TO ADDRESS CLINICAL PHARMACOLOGY QUESTIONS Key information needed from RWD to address research questions related to clinical pharmacology often includes: (i) patient characteristics (e. g. , age and sex), (ii) disease status (e. g. , diagnosis, severity, duration, and staging), (iii) medications used (e. g. , name, formulation, dose amount, frequency, duration, and prior lines of therapy), (iv) laboratory tests (e. g. , test type, methods, and results), and (v) other diagnostic tests (e. g. , biomarkers, genotypes, and tumor size). Common types of RWD that may provide this information include medical and pharmacy claims, patient-level data from EHRs, diagnostic laboratory tests, hospital chargemaster records, disease or product registries, and patient surveys, as well as data from medical and mobile/wearable devices.
Each type of RWD has strengths and limitations, and tradeoffs are often necessary when selecting a source of RWD for a study. Key attributes, strengths, and limitations of the three common RWD sources (i. e. , claims, EHRs, and patient registries) are summarized in Table  2  and discussed below. Common data elements found in different types of RWD are also summarized in Table  3. Databases with claims from large employers or health plans (e. g. , MarketScan and PharMetrics) can provide rich information about working-age adults but often exclude those covered by Medicaid, Medicare, or other government-sponsored programs in the United States. Conversely, claims data from government sources (e. g. , Medicare 5% national sample) may contain important information about adults over the age of 65 years, with permanent disability, or with certain chronic health conditions (e.