Prioritisation of data-poor pharmaceuticals for empirical testing and environmental risk assessment

被引:6
|
作者
Cannata, Cristiana [1 ]
Backhaus, Thomas [2 ]
Bramke, Irene [3 ]
Caraman, Maria [4 ]
Lombardo, Anna [5 ]
Whomsley, Rhys [4 ]
Moermond, Caroline T. A. [6 ]
Ragas, Ad M. J. [1 ]
机构
[1] Radboud Univ Nijmegen, Radboud Inst Biol & Environm Sci RIBES, Dept Environm Sci, Nijmegen, Netherlands
[2] Univ Gothenburg, Dept Biol & Environm Sci, Gothenburg, Sweden
[3] AstraZeneca, Global Sustainabil, The Hague, Netherlands
[4] European Med Agcy EMA, Amsterdam, Netherlands
[5] Ist Ric Farmacol Mario Negri IRCCS, Dept Environm Hlth Sci, Lab Environm Chem & Toxicol, Milan, Italy
[6] Natl Inst Publ Hlth & Environm RIVM, Ctr Safety Subst & Prod VSP, Bilthoven, Netherlands
关键词
Pharmaceuticals; Prioritization; Data gaps; Environmental risk assessment; ECOTOXICITY DATA; EXPOSURE; IMPACT;
D O I
10.1016/j.envint.2023.108379
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
There are more than 3,500 active pharmaceutical ingredients (APIs) on the global market for human and veterinary use. Residues of these APIs eventually reach the aquatic environment. Although an environmental risk assessment (ERA) for marketing authorization applications of medicinal products is mandatory in the European Union since 2006, an ERA is lacking for most medicines approved prior to 2006 (legacy APIs). Since it is unfeasible to perform extensive ERA tests for all these legacy APIs, there is a need for prioritization of testing based on the limited data available. Prioritized APIs can then be further investigated to estimate their environmental risk in more detail. In this study, we prioritized more than 1,000 APIs used in Europe based on their predicted risk for aquatic freshwater ecosystems. We determined their risk by combining an exposure estimate (Measured or Predicted Environmental Concentration; MEC or PEC, respectively) with a Predicted No Effect Concentration (PNEC). We developed several procedures to combine the limited empirical data available with in silico data, resulting in multiple API rankings varying in data needs and level of conservativeness. In comparing empirical with in silico data, our analysis confirmed that the PEC estimated with the default parameters used by the European Medicines Agency often - but not always - represents a worst-case scenario. Comparing the ecotoxicological data for the three main taxonomic groups, we found that fish represents the most sensitive species group for most of the APIs in our list. We furthermore show that the use of in silico tools can result in a substantial underestimation of the ecotoxicity of APIs. After combining the different exposure and effect estimates into four risk rankings, the top-ranking APIs were further screened for availability of ecotoxicity data in data repositories. This ultimately resulted in the prioritization of 15 APIs for further ecotoxicological testing and/or exposure assessment.
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页数:10
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