Framework to Evaluate Exposure Relevance and Data Needs for Risk Assessment of Nanomaterials using in Vitro Testing Strategies. Issue 8 (22nd February 2016)

• Record Type: Journal Article
• Authors: Sharma, Monita; Shatkin, Jo Anne; Cairns, Carolyn; Canady, Richard; Clippinger, Amy J.
• Is Part Of: Risk analysis. Volume 36:Issue 8(2016)
• Page Start: 1551
• Page End: 1563
• Publication Date: 2016-02-22
• Subjects: Exposure scenarios -- in vitro -- risk assessment -- testing strategies; Technology -- Risk assessment -- Periodicals; 658.403
• DOI: 10.1111/risa.12581
• Languages: English
• ISSNs: 0272-4332

Abstract

Abstract : This article presents a multistage framework for evaluating the strength of evidence of nanomaterial (NM) exposure characterization data to optimize the utility of in vitro testing strategies for human health risk assessment. This framework is intended to aid risk assessors in evaluating the relevance of data from in vitro tests and to optimize the development of new in vitro testing strategies. The initial stage frames the exposure scenarios of interest in advance of testing to incorporate aspects such as release points, route of exposure, biological and environmental transformations, dose metrics, and biological targets in subsequent stages. The second stage considers characterization in the context of a realistic exposure and the third stage involves designing a testing strategy based on expected exposure conditions. For the fourth and final stage, we propose a matrix approach to evaluate the strength of evidence obtained in the first three stages as a basis for determining the best combination of test conditions and analytical methods available to characterize and measure exposure based on the NM type. This approach can also be used to evaluate existing data for their relevance to the expected exposure scenario and to further develop and optimize in vitro testing strategies. Implementation of the proposed strategy will generate meaningful information on NM properties and their interaction with biological systems, based on realistic exposure scenarios, which will be cost effective and can be applied for assessing risk and making intelligent regulatory decisions regarding the use and disposal of NMs.