Phenoxymethylpenicillin potassium

Bioaccumulation: aquatic / sediment

Link to relevant study record(s)

Reference

Endpoint:

bioaccumulation in aquatic species, other

Remarks:

The bioconcentration factor was estimated.

Type of information:

(Q)SAR

Adequacy of study:

supporting study

Reliability:

2 (reliable with restrictions)

Rationale for reliability incl. deficiencies:

results derived from a (Q)SAR model, with limited documentation / justification, but validity of model and reliability of prediction considered adequate based on a generally acknowledged source

Qualifier:

no guideline followed

Principles of method if other than guideline:

The BCFBAF v3.01 program within the EpiSuite was used. The programme estimates BCF of an organic compound using the compound's log octanol-water partition coefficient (Kow). BCFBAF has been expanded to include estimation of the Biotransformation Rate (kM) in fish and estimation of Bioaccumulation Factor (BAF) by the Arnot-Gobas method (Arnot and Gobas, 2003).

GLP compliance:

no

Details on estimation of bioconcentration:

A brief summary of the estimation methodology:
The BCFBAF method classifies a compound as either ionic or non-ionic.  Ionic compounds include carboxylic acids, sulfonic acids and salts of sulfonic acids, and charged nitrogen compounds (nitrogen with a +5 valence such as quaternary ammonium compounds).  All other compounds are classified as non-ionic.
Training Dataset Included:
  466 Non-Ionic Compounds
  61  Ionic Compounds (carboxylic acids, sulfonic acids, quats)
Methodology for Non-Ionic was to separate compounds into three divisions by Log Kow value as follows:
  Log Kow  <  1.0
  Log Kow  1.0  to  7.0
  Log Kow  > 7.0

For Log Kow 1.0  to  7.0  the derived QSAR estimation equation is:
  Log BCF  =  0.6598 Log Kow  -  0.333  + Σcorrection factors
     (n = 396, r2 = 0.792, Q2 = 0.78, std dev = 0.511, avg dev = 0.395)
For Log Kow < 1.0  the derived QSAR estimation equation is:  All compounds with a log Kow of less than 1.0 are assigned an estimated log BCF of 0.50.
Ionic compounds are predicted as follows:
  log BCF  =  0.50    (log Kow  <  5.0)
  log BCF  =  1.00    (log Kow  5.0 to 6.0)
  log BCF  =  1.75    (log Kow  6.0 to 8.0)
  log BCF  =  1.00    (log Kow  8.0 to 9.0)
  log BCF  =  0.50    (log Kow   >  9.0)

Estimation Domain:
The minimum and maximum values for molecular weight and logKow are listed below.  Currently there is no universally accepted definition of model domain.  However, users may wish to consider the possibility that bioconcentration factor estimates are less accurate for compounds outside the MW and logKow ranges of the training set compounds, and/or that have more instances of a given correction factor than the maximum for all training set compounds.  It is also possible that a compound may have a functional group(s) or other structural features not represented in the training set, and for which no fragment coefficient was developed; and that a compound has none of the fragments in the model’s fragment library.  In the latter case, predictions are based on molecular weight alone.  These points should be taken into consideration when interpreting model results.
Training Set (527 Compounds):
Molecular Weight:
 Minimum MW:  68.08  (Furan)
 Maximum MW:  991.80   Ionic: (2,7-Naphthalenedisulfonic acid, 4-amino-5-hydroxy-3,6-
            bis[[4-[[2-(sulfooxy)ethyl]sulfonyl]phenyl]azo]-, tetrasodium salt)
 Maximum MW:  959.17   Non-Ionic: (Benzene, 1,1 -oxybis[2,3,4,5,6-pentabromo-)
 Average MW:  244.00
Log Kow:
 Minimum LogKow:  -6.50   Ionic: (2,7-Naphthalenedisulfonic acid, 4-amino-5-hydroxy-3,6-bis[[4-[[2-(sulfooxy)ethyl]sulfonyl]phenyl]azo]-, tetrasodium salt)
 Minimum LogKow:  -1.37   Non-Ionic: (1,3,5-Triazine-2,4,6-triamine)
 Maximum LogKow:  11.26 (Benzenamine, ar-octyl-N-(octylphenyl)-)
 
The Arnot-Gobas BCF and BAF model (Arnot and Gobas, 2003)
The Arnot-Gobas model estimates steady-state bioconcentration factor (BCF; L/kg) and bioaccumulation factor (BAF; L/kg) values for non-ionic organic chemicals in three general trophic levels of fish (i.e., lower, middle and upper) in temperate environments. The model calculations represent general trophic levels (i.e., not for a particular fish species) and are derived for “representative” environmental conditions (e.g., dissolved and particulate organic carbon content in the water column, water temperature). Thus, it provides general estimates for these conditions in absence of site-specific measurements or estimates. The default temperature for the BCF and BAF calculations is 10 °C (temperate regions); therefore, the model predictions are not recommended for arctic, sub-tropical or tropical regions or for comparisons with other vastly different conditions (e.g., laboratory tests at ~25 °C). Site-specific food web models, bioaccumulation models and bioconcentration models are available for specific modeling requirements (e.g., http://www.rem.sfu.ca/toxicology/models/models.htm , http://www.trentu.ca/cemc).
The model includes mechanistic processes for bioconcentration and bioaccumulation such as chemical uptake from the water at the gill surface (BCFs and BAFs) and the diet (BAFs only), and chemical elimination at the gill surface, fecal egestion, growth dilution and metabolic biotransformation (Arnot and Gobas 2003). Other processes included in the calculations are bioavailability in the water column (only the freely dissolved fraction can bioconcentrate) and absorption efficiencies at the gill and in the gastrointestinal tract. The model requires the octanol-water partition coefficient (KOW) of the chemical and the normalized whole-body metabolic biotransformation rate constant (kM, N; /day) as input parameters to predict BCF and BAF values. The required kM, N value must be normalized to a fish of 10 g (Arnot et al., 2008). Model predictions may be highly uncertain for chemicals that have estimated log KOW values > 9. The model is not recommended at this time for chemicals that appreciably ionize, for pigments and dyes, or for perfluorinated substances.
The BAF calculations were derived from the parameterization and calibration of the model to a large database of measured BAF values from the Great Lakes (Lake Ontario, Lake Erie and Lake St. Clair). The measured BAFs are for chemicals that are poorly metabolized (e.g., PCBs) and were generally grouped into lower, middle and upper trophic levels of fish species. The overall food web biomagnification factors (β) in the BAF model are calibrated to each trophic level of measured BAF values (Arnot and Gobas, 2003). Therefore, in the absence of metabolic biotransformation the BAF model predictions are in general agreement with measured BAFs in fish of these general trophic positions from the Great Lakes for chemicals that are poorly metabolized.
Other considerations in using Arnot-Gobas BAF-BCF
The model may not adequately capture biotransformation at the first trophic level for chemicals that are readily biotransformed in invertebrates and plankton.  Unfortunately, there is not available a kM prediction model for invertebrates and plankton that adequately captures possible biotransformation in these lower trophic-level positions; i.e. diet for low- and mid-trophic level fish.  Therefore, the model may be somewhat conservative in that it assumes negligible biotransformation for invertebrates and plankton.  However, the model does give users a sense of the range of BAFs that might be observed in the environment, and this is useful because an uncertainty analysis is not included for kM, Kow, trophic position etc.  Also, an underlying issue often is whether biomagnification or biodilution is more likely for a given chemical, and including three trophic levels allows at least some insight to be gained.
In the environment there are multiple trophic levels of fish and other aquatic organisms, and both biomagnification and trophic dilution are observed.  When chemicals biomagnify the highest BAFs are at higher trophic levels; conversely when chemicals are readily biotransformed, the highest BAFs are at lower trophic levels.  The model appears to capture these phenomena.
The Arnot-Gobas model assumes default lipid contents of 10.7%, 6.85% and 5.98% for the upper, middle and lower trophic levels, respectively, as given in Appendix K.  Since the lab studies from which most data in the measured BCF database were derived typically used fish with 3-5% lipid content, this may help explain why the regression-based BCF model typically yields estimated BCF values lower than from the Arnot-Gobas model.  A reasonable way to compare BCF and BAF values across measured and estimated data is to convert them to 100% lipid basis.  This can be done if the % lipid is known or can be estimated, by dividing the wet weight BCF (units of L/kg) by the % lipid expressed as a fraction.  For example, a BCF of 5,000 based on wet weight for a fish with 10% lipid is 5,000 L/kg divided by 0.1 = 50,000 L/kg lipid weight.
 

Key result

Type:

BCF

Value:

3.16 L/kg

Basis:

not specified

Remarks on result:

other: All compounds with a log Kow of less than 1.0 are assigned an estimated log BCF of 0.50.

Conclusions:

The estimated log BCF is 0.5 L/kg wet weight.

Executive summary:

The bioconcentration factor BCF was estimated using the BCFBAF v3.01 model within the EpiSuite programme. The estimated log BCF is 0.5 L/kg wet weight.

Description of key information

According to column 2 of Annex IX of REACH "The study need not be conducted if:

— the substance has a low potential for bioaccumulation (for instance a log Kow = 3) ..."
The test item has a low partition coefficient of logKow = -3.4.

Key value for chemical safety assessment

BCF (aquatic species):

3.162 L/kg ww