How should ethnicity-related information be included on drug labels? Considerations based on comparison of multiregional clinical trial data on the label between Japan and the United States

2015 ◽  
Vol 98 (5) ◽  
pp. 480-482 ◽  
Author(s):  
A Tanaka ◽  
K Asano ◽  
Y Uyama
Keyword(s):  
United States ◽  
Clinical Trial ◽  
Clinical Trial Data ◽  
The United States ◽  
Trial Data ◽  
Related Information

Toward a Jurisprudence of Drug Regulation

2014 ◽  
Vol 42 (2) ◽  
pp. 244-262 ◽  
Author(s):  
Matthew Herder
Keyword(s):  
United States ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Medical Devices ◽  
Phase 1 ◽  
Clinical Trial Data ◽  
The United States ◽  
Trial Data ◽  
Regulatory Decisions ◽  
Clinical Study Reports

Efforts to ensure greater transparency in the regulation of “drugs” (used here as a catch-all for pharmaceuticals, biologics, medical devices, and biomarker-based technologies such as genetic testing paired with a pharmaceutical or biologic) are well underway. For example, laws in the United States and Europe now require registration of most clinical trials beyond phase 1. Yet instances of avoidable harm to patients continue to arise. In response, calls for disclosure of clinical trial data in the form of “clinical study reports,” not just trial designs and basic results, are growing. In this paper, I argue that disclosure of clinical trial data is necessary but insufficient. Rather, the regulatory decisions that flow from those trial data —whether positive (i.e., product approvals) or negative (i.e., abandoned products, product refusals, and withdrawals) —should also be open to outside scrutiny provided they are final in nature.

scholarly journals Data Exclusivities in the Age of Big Data, Biologics, and Plurilaterals

2019 ◽  
Vol 6 (4) ◽  
pp. 22-33
Author(s):  
Peter K. Yu
Keyword(s):  
Clinical Trial ◽  
Big Data ◽  
Intellectual Property ◽  
Clinical Trial Data ◽  
Trade Agreements ◽  
The United States ◽  
Trial Data ◽  
Marketing Approval ◽  
Clinical Practices ◽  
Pharmaceutical Industries

The past decade has seen many new developments impacting the intellectual property system. The introduction of big data analytics has transformed the fields of biotechnology and bioinformatics while ushering in major advances in drug development, clinical practices, and medical financing. The arrival of biologics and personalized medicines has also revolutionized the healthcare and pharmaceutical industries. In addition, the emergence of bilateral, regional, and plurilateral trade agreements have raised serious, and at times difficult, questions concerning the evolution of domestic and international intellectual property standards. One topic linking all three developments together concerns the establishment of international standards to protect clinical trial data that have been submitted to regulatory authorities for the marketing approval of pharmaceutical products. During the negotiations for the Trans-Pacific Partnership (TPP), for example, the protection of clinical trial data submitted for the marketing approval of biologics was highly contentious. Although the United States’ withdrawal in January 2017 has since placed the TPP Agreement and its data exclusivity provisions for pharmaceuticals and biologics on life support, the debate on the protection of clinical trial data will continue and will emerge in future bilateral, regional, and plurilateral trade negotiations, including the renegotiations on the North American Free Trade Agreement (NAFTA). Part I of this Article reviews the protection of clinical trial data under Article 39.3 of the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS Agreement) of the World Trade Organization (WTO). Even though the provision covers both pharmaceutical and agricultural chemical products, this Article focuses only on the former. Part II examines the additional protection clinical trial data have received through TRIPS-plus bilateral, regional, and plurilateral trade agreements. Part III outlines five specific recommendations to help advance the debate on such protection in the age of big data, biologics, and plurilateral trade agreements.

scholarly journals Revised Risk Classification for Pediatric Extracranial Germ Cell Tumors Based on 25 Years of Clinical Trial Data From the United Kingdom and United States

2015 ◽  
Vol 33 (2) ◽  
pp. 195-201 ◽  
Author(s):  
A. Lindsay Frazier ◽  
Juliet P. Hale ◽  
Carlos Rodriguez-Galindo ◽  
Ha Dang ◽  
Thomas Olson ◽  
...  
Keyword(s):  
United States ◽  
Clinical Trial ◽  
United Kingdom ◽  
Germ Cell ◽  
Germ Cell Tumors ◽  
Stage Iv ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Tumor Site ◽  
Data Set

Purpose To risk stratify malignant extracranial pediatric germ cell tumors (GCTs). Patients and Methods Data from seven GCT trials conducted by the Children's Oncology Group (United States) or the Children's Cancer and Leukemia Group (United Kingdom) between 1985 and 2009 were merged to create a data set of patients with stage II to IV disease treated with platinum-based therapy. A parametric cure model was used to evaluate the prognostic importance of age, tumor site, stage, histology, tumor markers, and treatment regimen and estimate the percentage of patients who achieved long-term disease-free (LTDF) survival in each subgroup of the final model. Validation of the model was conducted using the bootstrap method. Results In multivariable analysis of 519 patients with GCTs, stage IV disease (P = .001), age ≥ 11 years (P < .001), and tumor site (P < .001) were significant predictors of worse LTDF survival. Elevated alpha-fetoprotein (AFP) ≥ 10,000 ng/mL was associated with worse outcome, whereas pure yolk sac tumor (YST) was associated with better outcome, although neither met criteria for statistical significance. The analysis identified a group of patients age > 11 years with either stage III to IV extragonadal tumors or stage IV ovarian tumors with predicted LTDF survival < 70%. A bootstrap procedure showed retention of age, tumor site, and stage in > 94%, AFP in 12%, and YST in 27% of the replications. Conclusion Clinical trial data from two large national pediatric clinical trial organizations have produced a new evidence-based risk stratification of malignant pediatric GCTs that identifies a poor-risk group warranting intensified therapy.

Clinical Trial Data Management Software: A Review of the Technical Features

2019 ◽  
Vol 14 (3) ◽  
pp. 160-172 ◽  
Author(s):  
Aynaz Nourani ◽  
Haleh Ayatollahi ◽  
Masoud Solaymani Dodaran
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Data Management ◽  
Clinical Data ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Data Management System ◽  
Management Systems ◽  
Data Management Systems ◽  
Technical Features

Background:Data management is an important, complex and multidimensional process in clinical trials. The execution of this process is very difficult and expensive without the use of information technology. A clinical data management system is software that is vastly used for managing the data generated in clinical trials. The objective of this study was to review the technical features of clinical trial data management systems.Methods:Related articles were identified by searching databases, such as Web of Science, Scopus, Science Direct, ProQuest, Ovid and PubMed. All of the research papers related to clinical data management systems which were published between 2007 and 2017 (n=19) were included in the study.Results:Most of the clinical data management systems were web-based systems developed based on the needs of a specific clinical trial in the shortest possible time. The SQL Server and MySQL databases were used in the development of the systems. These systems did not fully support the process of clinical data management. In addition, most of the systems lacked flexibility and extensibility for system development.Conclusion:It seems that most of the systems used in the research centers were weak in terms of supporting the process of data management and managing clinical trial's workflow. Therefore, more attention should be paid to design a more complete, usable, and high quality data management system for clinical trials. More studies are suggested to identify the features of the successful systems used in clinical trials.

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