Access and Unmet Needs of Orphan Drugs in 194 Countries and 6 Areas: A Global Policy Review With Content Analysis.

OBJECTIVES: Three hundred million people living with rare diseases worldwide are disproportionately deprived of in-time diagnosis and treatment compared with other patients. This review provides an overview of global policies that optimize development, licensing, pricing, and reimbursement of orphan drugs. METHODS: Pharmaceutical legislation and policies related to access and regulation of orphan drugs were examined from 194 World Health Organization member countries and 6 areas. Orphan drug policies (ODPs) were identified through internet search, emails to national pharmacovigilance centers, and systematic academic literature search. Texts from selected publications were extracted for content analysis. RESULTS: One hundred seventy-two drug regulation documents and 77 academic publications from 162 countries/areas were included. Ninety-two of 200 countries/areas (46.0%) had documentation on ODPs. Thirty-four subthemes from content analysis were categorized into 6 policy themes, namely, orphan drug designation, marketing authorization, safety and efficacy requirements, price regulation, incentives that encourage market availability, and incentives that encourage research and development. Countries/areas with ODPs were statistically wealthier (gross national income per capita = $10 875 vs$3950, P < .001). Country/area income was also positively correlated with the scope of the respective ODP (correlation coefficient = 0.57, P < .001). CONCLUSIONS: Globally, the number of countries with an ODP has grown rapidly since 2013. Nevertheless, disparities in geographical distribution and income levels affect the establishment of ODPs. Furthermore, identified policy gaps in price regulation, incentives that encourage market availability, and incentives that encourage research and development should be addressed to improve access to available and affordable orphan drugs

Characteristics and outcomes of 627 044 COVID-19 patients living with and without obesity in the United States, Spain, and the United Kingdom

Altres ajuts: This research received partial support from the National Institute for Health Research (NIHR) Oxford Biomedical Research Center (BRC), US National Institutes of Health, US Department of Veterans Affairs, Janssen Research & Development, and IQVIA. The University of Oxford received funding related to this work from the Bill & Melinda Gates Foundation (Investment ID INV016201 and INV-019257). APU has received funding from the Medical Research Council (MRC) [MR/K501256/1, MR/N013468/1] and Fundación Alfonso Martín Escudero (FAME) (APU). VINCI [VA HSR RES 13-457] (SLD, MEM, KEL). JCEL has received funding from the Medical Research Council (MR/K501256/1) and Versus Arthritis (21605). MR is funded by Wereld Kanker Onderzoek Fonds (WKOF), as part of the World Cancer Research Fund International grant program [grant number: 2017/1630]A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of patients living without obesity. We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes (hospitalization, events, and death) by obesity status. We included 627 044 (Spain: 122 058, UK: 2336, and US: 502 650) diagnosed and 160 013 (Spain: 18 197, US: 141 816) hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized (39.9%, 95%CI: 39.8−40.0) than among those diagnosed with COVID-19 (33.1%; 95%CI: 33.0−33.2). In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity. We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies

Clinical Characterization of Patients Diagnosed with Prostate Cancer and Undergoing Conservative Management:A PIONEER Analysis Based on Big Data

Background: Conservative management is an option for prostate cancer (PCa) patients either with the objective of delaying or even avoiding curative therapy, or to wait until palliative treatment is needed. PIONEER, funded by the European Commission Innovative Medicines Initiative, aims at improving PCa care across Europe through the application of big data analytics. Objective: To describe the clinical characteristics and long-term outcomes of PCa patients on conservative management by using an international large network of real-world data. Design, setting, and participants: From an initial cohort of &gt;100 000 000 adult individuals included in eight databases evaluated during a virtual study-a-thon hosted by PIONEER, we identified newly diagnosed PCa cases (n = 527 311). Among those, we selected patients who did not receive curative or palliative treatment within 6 mo from diagnosis (n = 123 146). Outcome measurements and statistical analysis: Patient and disease characteristics were reported. The number of patients who experienced the main study outcomes was quantified for each stratum and the overall cohort. Kaplan-Meier analyses were used to estimate the distribution of time to event data. Results and limitations: The most common comorbidities were hypertension (35–73%), obesity (9.2–54%), and type 2 diabetes (11–28%). The rate of PCa-related symptomatic progression ranged between 2.6% and 6.2%. Hospitalization (12–25%) and emergency department visits (10–14%) were common events during the 1st year of follow-up. The probability of being free from both palliative and curative treatments decreased during follow-up. Limitations include a lack of information on patients and disease characteristics and on treatment intent. Conclusions: Our results allow us to better understand the current landscape of patients with PCa managed with conservative treatment. PIONEER offers a unique opportunity to characterize the baseline features and outcomes of PCa patients managed conservatively using real-world data. Patient summary: Up to 25% of men with prostate cancer (PCa) managed conservatively experienced hospitalization and emergency department visits within the 1st year after diagnosis; 6% experienced PCa-related symptoms. The probability of receiving therapies for PCa decreased according to time elapsed after the diagnosis.</p

Characteristics and outcomes of over 300,000 patients with COVID-19 and history of cancer in the United States and Spain

Background: We described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza. Methods: We conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes. Results: We included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%–18% and 1%–14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin’s lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n ¼ 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events. Conclusions: Patients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent. Impact: This study provides epidemiologic characteristics that can inform clinical care and etiologic studies.</p

Tunable acoustic passive phased array based on double-opening resonant rings

The special structural design of acoustic metamaterials further extends acoustic properties of the materials. We design a tunable acoustic passive phased array based on double-opening resonant rings, which modulates the acoustic waves only by the rotational angle, making up for the defect of the fixed structure of ordinary metamaterials. The rotation angle is selected based on the generalized Snell’s law, which not only enables focusing in a large frequency band range but also meets the focusing demand of acoustic waves incident at different angles and controls the position of the focal point

Letters to the Editor: Refractory SAPHO syndrome with positive Th17-related pathway immunohistochemistry staining

A 34-year-old female was presented with left clavicle mass, recurrent ostalgia, and vesiculo-pustular rash in hands and feet. During 9 years of disease duration, she received first- and second-line therapies, however, with poor respond to non-steroidal anti-inflammatory drugs and disease-modifying antirheumatic drugs. Upon arrival, the physical examination revealed right clavicle swollen with severe tenderness and poor lumbar motility. Laboratory evaluation revealed elevated inflammatory indicators and imaging examinations revealed multiple bone erosion and sclerosis. The immunohistochemical staining of the bone biopsy presented the elevation of the Th17-related cytokines interleukin (IL)-6, IL-17, and IL-23, and negative expression of tumor necrosis factor (TNF)-β and receptor activator NK-κB ligand (RANKL). The immunohistochemical staining of target tissue revealed possible physiologic pathway and potential treatment target. Th17-related pathway might function as the central pathway of pathophysiologic process in SAPHO syndrome

A bibliometric analysis of worldwide cancer research using machine learning methods

Abstract With the progress and development of computer technology, applying machine learning methods to cancer research has become an important research field. To analyze the most recent research status and trends, main research topics, topic evolutions, research collaborations, and potential directions of this research field, this study conducts a bibliometric analysis on 6206 research articles worldwide collected from PubMed between 2011 and 2021 concerning cancer research using machine learning methods. Python is used as a tool for bibliometric analysis, Gephi is used for social network analysis, and the Latent Dirichlet Allocation model is used for topic modeling. The trend analysis of articles not only reflects the innovative research at the intersection of machine learning and cancer but also demonstrates its vigorous development and increasing impacts. In terms of journals, Nature Communications is the most influential journal and Scientific Reports is the most prolific one. The United States and Harvard University have contributed the most to cancer research using machine learning methods. As for the research topic, “Support Vector Machine,” “classification,” and “deep learning” have been the core focuses of the research field. Findings are helpful for scholars and related practitioners to better understand the development status and trends of cancer research using machine learning methods, as well as to have a deeper understanding of research hotspots

Artificial Intelligence Supports Research Progress in the Diagnosis and Treatment of Rare Diseases

It is noteworthy that only 5% of more than 7000 described rare diseases are treated. In the era of big data, there is ever-increasing data for understanding biomedicine. The need for efficient and rapid data collection, analyses and characterization methods is pressing. Rare diseases can particularly benefit from artificial intelligence (AI) application. AI, with an emphasis on machine learning, creates a path for such efforts and is being applied to diagnosis and treatment. AI has demonstrated its potential to learn and analyze data from different sources with results in prediction。Presently, there are AI-driven technologies applied for rare diseases and this review aims to summarize these advances. Moreover, this review scrutinizes the limitation and identifies the pitfalls of AI applications in the diagnosis and treatment of rare diseases

Correlation and precision of GFR estimation by different equations.

<p>Correlation and precision of GFR estimation by different equations.</p