Literature DB >> 16211163

Managing severe acute respiratory syndrome (SARS) intellectual property rights: the possible role of patent pooling.

James H M Simon1, Eric Claassen, Carmen E Correa, Albert D M E Osterhaus.   

Abstract

Patent applications that incorporate the genomic sequence of the severe acute respiratory syndrome (SARS) coronavirus, have been filed by a number of organizations. This is likely to result in a fragmentation of intellectual property (IP) rights which in turn may adversely affect the development of products, such as vaccines, to combat SARS. Placing these patent rights into a patent pool to be licensed on a non-exclusive basis may circumvent these difficulties and set a key precedent for the use of this form of mechanism in other areas of health care, leading to benefits to public health.

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Year:  2005        PMID: 16211163      PMCID: PMC2626342          DOI: /S0042-96862005000900017

Source DB:  PubMed          Journal:  Bull World Health Organ        ISSN: 0042-9686            Impact factor:   9.408


  5 in total

Review 1.  Searching for animal models and potential target species for emerging pathogens: Experience gained from Middle East respiratory syndrome (MERS) coronavirus.

Authors:  Júlia Vergara-Alert; Enric Vidal; Albert Bensaid; Joaquim Segalés
Journal:  One Health       Date:  2017-03-03

Review 2.  Defining the spectrum of genome policy.

Authors:  Susanne B Haga; Huntington F Willard
Journal:  Nat Rev Genet       Date:  2006-12       Impact factor: 53.242

Review 3.  Patent pools and clearinghouses in the life sciences.

Authors:  Esther van Zimmeren; Sven Vanneste; Gert Matthijs; Wim Vanhaverbeke; Geertrui Van Overwalle
Journal:  Trends Biotechnol       Date:  2011-07-04       Impact factor: 19.536

Review 4.  Patent pools and diagnostic testing.

Authors:  Birgit Verbeure; Esther van Zimmeren; Gert Matthijs; Geertrui Van Overwalle
Journal:  Trends Biotechnol       Date:  2006-01-27       Impact factor: 19.536

5.  Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning.

Authors:  Alejandro Lopez-Rincon; Alberto Tonda; Lucero Mendoza-Maldonado; Daphne G J C Mulders; Richard Molenkamp; Carmina A Perez-Romero; Eric Claassen; Johan Garssen; Aletta D Kraneveld
Journal:  Sci Rep       Date:  2021-01-13       Impact factor: 4.379

  5 in total

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