| Literature DB >> 33324212 |
Chieko Kurihara1, Varvara Baroutsou2, Sander Becker3, Johan Brun4, Brigitte Franke-Bray5, Roberto Carlesi6, Anthony Chan7, Luis Francisco Collia8, Peter Kleist9, Luís Filipe Laranjeira10, Kotone Matsuyama11, Shehla Naseem12, Johanna Schenk13, Honorio Silva14, Sandor Kerpel-Fronius15.
Abstract
Expansion of data-driven research in the 21st century has posed challenges in the evolution of the international agreed framework of research ethics. The World Medical Association (WMA)'s Declaration of Helsinki (DoH) has provided ethical principles for medical research involving humans since 1964, with the last update in 2013. To complement the DoH, WMA issued the Declaration of Taipei (DoT) in 2016 to provide additional principles for health databases and biobanks. However, the ethical principles for secondary use of data or material obtained in research remain unclear. With such a perspective, the Working Group on Ethics (WGE) of the International Federation of Associations of Pharmaceutical Physicians and Pharmaceutical Medicine (IFAPP) suggests a closer scientific linkage in the DoH to the (Declaration of Taipei) DoT focusing specifically on areas that will facilitate data-driven research, and to further strengthen the protection of research participants.Entities:
Keywords: Declaration of Helsinki; Declaration of Taipei; data science; data sharing; medicines development; privacy protection; research ethics
Year: 2020 PMID: 33324212 PMCID: PMC7723451 DOI: 10.3389/fphar.2020.579714
Source DB: PubMed Journal: Front Pharmacol ISSN: 1663-9812 Impact factor: 5.810
Examples of health databases and biobanks and examples of their utilization.
| Types of health databases and biobanks with brief explanations | Related examples of expected R&D activities |
|---|---|
|
| Drug development lead candidate search |
|
| Rare disease drug development including lead candidate search. Alternative to control group of a clinical trial |
|
| New Drug Application (NDA) for new indication. Post-Marketing Surveillance (PMS) after expedited approval. Artificial Intelligence (AI) development |
|
| Individual Participant Data (IPD) meta-analysis. Subgroup analysis of clinical trial results |
Benefits and risks of IPD sharing.
| Benefits/merits | Risks/demerits |
|---|---|
| “Maximize the knowledge gained from the efforts and sacrifices of clinical trial participants” (ICMJE) | Privacy risk of participants unless data to be shared would be “de-identified” participant data |
| “Strengthening the science that is the foundation of safe and effective clinical care and public health practice” (CIOMS) | Risk to researcher/sponsor of impact of re-analysis on their original finding or commercial interests |
| Possibility of independent re-analysis of clinical trial results, including systematic review as well as subgroup analysis for personalized medicine | Risk to public health - impact of unfair/invalid secondary analysis |
| Increase the transparency and credibility of clinical trials | Burden of researchers to prepare their data/material obtained in their research in format possible to be shared with others |
Summarized from the statements of the organizations cited in this manuscript.