| 2. |
- Cannata, Cristiana, et al.
(author)
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Prioritisation of data-poor pharmaceuticals for empirical testing and environmental risk assessment
- 2024
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In: Environment International. - 0160-4120 .- 1873-6750. ; 183
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Journal article (peer-reviewed)abstract
- 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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| 3. |
- Moermond, Caroline T. A., et al.
(author)
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CRED : CRITERIA FOR REPORTING AND EVALUATING ECOTOXICITY DATA
- 2016
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In: Environmental Toxicology and Chemistry. - : Wiley. - 0730-7268 .- 1552-8618. ; 35:5, s. 1297-1309
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Journal article (peer-reviewed)abstract
- Predicted-no-effect concentrations (PNECs) and environmental quality standards (EQSs) are derived in a large number of legal frameworks worldwide. When deriving these safe concentrations, it is necessary to evaluate the reliability and relevance of ecotoxicity studies. Such evaluation is often subject to expert judgment, which may introduce bias and decrease consistency when risk assessors evaluate the same study. The Criteria for Reporting and Evaluating Ecotoxicity Data (CRED) project attempts to address this problem. It aims to improve the reproducibility, transparency, and consistency of reliability and relevance evaluations of aquatic ecotoxicity studies among regulatory frameworks, countries, institutes, and individual assessors. In the present study, the CRED evaluation method is presented. It includes a set of 20 reliability and 13 relevance criteria, accompanied by extensive guidance. Risk assessors who participated in the CRED ring test evaluated the CRED evaluation method to be more accurate, applicable, consistent, and transparent than the often-used Klimisch method. The CRED evaluation method is accompanied by reporting recommendations for aquatic ecotoxicity studies, with 50 specific criteria divided into 6 categories: general information, test design, test substance, test organism, exposure conditions, and statistical design and biological response. An ecotoxicity study in which all important information is reported is more likely to be considered for regulatory use, and proper reporting may also help in the peer-review process.
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