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In SECIS nine substances have been classified as either a moderate or a high environmental risk ie they have a PEC/PNEC ratio greater than one. These are: allopurinol, amoxicillin, estradiol, ethinylestradiol, mykofenolatmofetil, natriummykofenolat, raloxifene and sertraline.
Both the EMA guideline (EMA, 2006), and the TGD (European Commission, 2003) requires an taking tramadol and ambien in the same day extended environmental fate and effect analysis for substance with a PEC/ mrthadone and xanax detox PNEC ratio greater than one, while this is not required in the SECIS guidance document. The pharmaceutical industry has reported that environmental data is lacking for a substantial number of their products, i.e. 59 % of mrthadone and xanax detox the assessed products could not be classified since they lack sufficient ecotoxicity data. (In addition, 47 % lack data to enable a classification of the substances' persistency, and 40 % lack bioaccumulation data). Evaluation of the system's achievements so far There are two main aspects of SECIS considered in this chapter, namely data selection and completeness of the data-set. Data selection concerns how data were selected for the risk assessments made so far, and in particular, what types of data are included (e.g.
standard or non-standard, short-term or long-term).
The completeness of the data-set concerns whether all available data have been included and if not, whether a supplemented data-set would alter the PEC/PNEC ratio and the classification. For these analyses all the mrthadone and xanax detox pharmaceutical substances classified by at least mrthadone and xanax detox one company (available at www.fass.se April 2009) and for which ecotoxicity data could be found in the open scientific literature were selected. This resulted in a set of 36 substances and 48 assessments that were scrutinized. Data selection Each of the 48 assessments was compared in detail to the requirements of the SECIS guidance document. The comparison focused on the selection of ecotoxicity data. The three SECIS guidance document recommendations considered in this evaluation are: 1.SECIS allows for both standard and non-standard test data when calculating PNEC. 2.Long-term ecotoxicity data are preferred when calculating the PNEC. 3.Companies are recommended to take external ecotoxicity data into account when classifying their products. (For the full analyses, including additional SECIS guidance document recommendations, see Agerstrand and Ruden, 2010). The 36 substances selected for evaluation according to the selection criteria defined above, have been risk assessed by 60 companies. 48 of these 60 assessments resulted in a classification. The remaining 12 assessments did not result in a classification due to lack of data. Below are the results from the comparison of the 48 risk assessments to the three guidance recommendations: 1: SECIS allows for both standardized (such as OECD, FDA, ASTM, and ISO) and non-standardized data when calculating PNEC. In 33 (69%) of the 48 assessments standard ecotoxicity data was used when calculating PNEC.
Non-standard ecotoxicity data was used in 6 assessments (12%), and in 9 cases it was unclear whether the data originated from standard or non-standard tests. 2: Long-term ecotoxicity data are preferred when calculating PNEC. The classifications were based on long-term data in 9 (19%) of the 48 risk assessments. In one case the type of data was unclear and in 38 assessments (79%) PNEC was calculated using short-term data. 3: Companies are recommended to take publicly available ecotoxicity data into account when classifying their products. Ecotoxicity data from the open scientific literature was used in 20 (42%) of 5 buy tramadol online the 48 risk assessments. In 16 of the 20 cases these data were used when calculating PNEC. In 27 of the 48 cases the risk assessment was based on company-owned data only, and in one case the source of the data was unclear. From this exercise it can be seen that standard test data and short-term data are more commonly referred to compared to non-standard, and long-term data.
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