A quantitative risk assessment method for synthetic biology products in the environment.

Environmental health and safety Risk assessment Synthetic biology

Journal

The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500

Informations de publication

Date de publication:
15 Dec 2019
Historique:
received: 10 07 2019
revised: 13 08 2019
accepted: 14 08 2019
pubmed: 26 8 2019
medline: 1 2 2020
entrez: 26 8 2019
Statut: ppublish

Résumé

The need to prevent possible adverse environmental health impacts resulting from synthetic biology (SynBio) products is widely acknowledged in both the SynBio risk literature and the global regulatory community. To-date, however, discussions of potential risks of SynBio products have been largely speculative, and the limited attempts to characterize the risks of SynBio products have been non-uniform and entirely qualitative. As the SynBio discipline continues to accelerate and bring forth novel, highly-engineered life forms, a standardized risk assessment framework will become critical for ensuring that the environmental risks of these products are characterized in a consistent, reliable, and objective manner that incorporates all SynBio-unique risk factors. In their current forms, established risk assessment frameworks - including those that address traditional genetically modified organisms - fall short of the features required of this standard framework. To address this gap, we propose the Quantitative Risk Assessment Method for Synthetic Biology Products (QRA-SynBio) - an incremental build on established risk assessment methodologies that supplements traditional paradigms with the SynBio risk factors that are currently absent, and necessitates quantitative analysis for more transparent and objective risk characterizations. We demonstrate through a hypothetical case study that the proposed framework facilitates defensible quantification of the environmental risks of SynBio products in both foreseeable and hypothetical use scenarios. Additionally, we show how the quantitative nature of the proposed method can promote increased experimental investigation into the true likelihood of hazard and exposure parameters and highlight the most sensitive parameters where uncertainty should be reduced, ultimately leading to more targeted SynBio risk research and yielding more precise characterizations of risk.

Identifiants

pubmed: 31446290
pii: S0048-9697(19)33910-5
doi: 10.1016/j.scitotenv.2019.133940
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

133940

Informations de copyright

Published by Elsevier B.V.

Auteurs

Taylor Rycroft (T)

Environmental Laboratory, U.S. Army Engineer Research and Development Center, Concord, MA, USA. Electronic address: taylor.e.rycroft@usace.army.mil.

Kerry Hamilton (K)

School for Sustainable Engineering and the Built Environment & The Biodesign Institute Center for Environmental Health Engineering, Arizona State University, Tempe, AZ, USA.

Charles N Haas (CN)

Department of Civil, Architectural and Environmental Engineering, Drexel University, Philadelphia, PA, USA.

Igor Linkov (I)

Environmental Laboratory, U.S. Army Engineer Research and Development Center, Concord, MA, USA.

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