Daphnia magna EC50 24h




The acute aquatic toxicity model predicts the effect concentration of a substance that causes adverse affects on 50% (EC50) of the test population Daphnia magna within a designated period.




The training set consists of EC50 values for 119 chemicals [1-7]:


  • Test duration - 24 hours,
  • Test species - Daphnia magna (water flea).


The tested chemicals belong to the following categories:


  • Narcotic toxicants - 75 chemicals,
  • Reactive unspecified chemicals - 43 chemicals.




The organism response to the presence of toxicant in the environment is considered as a consequence of the combined influence of two different processes: uptake of the chemical into the biophase and interaction with the site of action [8]. In the present model, the uptake is modeled by maximum potential of the toxicant to bioconcentrate in the fish, while the interaction of chemicals is explained by descriptors assessing the electrophilic character of the molecule [9].  Such descriptors could include the energy of the lowest unoccupied molecular orbital, electronegativity, average or maximum superdelocalizability, maximum charge at non-hydrogen atom, etc.  The following model was developed based on regression analysis of the data:


log 1/EC50 = 1.55(±0.11)+1.24(±0.05) logBCFmax -0.13(±0.03)ELUMO


where BCFMax is the maximum bioconcentration factor [10], and ELUMO is the energy of the lowest unoccupied molecular orbital.  For the reactive unspecified chemicals, only the minimum toxicity is determined based on the model for narcotic chemicals (i.e.,  log 1/EC50 ≥ log 1/EC50 of narcotics).




The stepwise approach [11] was used to define the applicability domain of the model.  It consists of the following sub-domain levels:


  • General parametric requirements - includes ranges of variation of log KOW and MW,
  • Structural domain - based on atom-centered fragments (ACFs).


A chemical is considered In Domain if its log KOW and MW are within the specified ranges and its ACFs are presented in the training chemicals.  The information implemented in the applicability domain is extracted from the correctly predicted training chemicals used to build the model and in this respect the applicability domain determines practically the interpolation space of the model.




The precision of the regression model is characterized by the following estimates:


  • 95% Confidence intervals of model parameters,
  • Coefficient of determination R2 = 0.93,
  • Mean squared error (estimate of error variance) s2 = 0.14,
  • F value = 294.8,
  • Number of chemicals, n = 75.




1. Zhao YH, Ji GD, Cronin MTD, Dearden JC. 1998. QSAR study of the toxicity of benzoic acids to Vibrio fischeri, Daphnia magna and carp. The Science of the Total Environment 216: 205-215.
2. Schuurmann G. 1998. Ecotoxic Modes of Action of Chemical Substances. 22.
3. D. Robert, R. Carbo-Dorca, Aromatic compounds aquatic toxicity QSAR using molecular quantum similarity measures, SAR QSAR Environ. Res. 10 (1999) 401-422.
4. Zhao YH, Cronin MTD, Dearden JC. 1998. Quantitative Structure-Activity Relationships of Chemicals Acting by Non-polar Narcosis- Theoretical Considerations. Quant. Struct.-Act. Relat. 17: 131-138.
5. Ramos EU. 1998. Aquatic Toxicity of Polar Narcotic Pollutants. Mechanism, Modeling and Environmental Effect Assessment. Ph.D Thesis.
6. Estimating environmentally important properties of chemicals from the chemical structure - Erik Furusjo, Magnus Andersson, Magnus Rahmberg, Anders Svenson - Report B1517.
7. Ohe PC, Kuhne R, Ebert R-U, Altenburger R, Liess M, Schuurmann G. 2005.Structural Alerts-A New Classification Model to Discriminate Excess Toxicity from Narcotic Effect Levels of Organic Compounds in the Acute Daphnid Assay. Chem. Resear. Toxicol. 18(3): 536-555.
8. J.W. McFarland, J. Med. Chem. 13 (1970) 1092-1196.
9. S.D. Dimitrov, O.G. Mekenyan, G.D. Sinks, T.W. Schultz, Journal of Molecular Structure (Theochem, 622 (2003) 63-70.
10. S. Dimitrov, N. Dimitrova, D. Georgieva, K. Vasilev, T. Hatfield, J. Straka, O. Mekenyan, SAR and QSAR in Environmental Research, 23 (2012) 17-36.
11. S. Dimitrov, G. Dimitrova, T. Pavlov, N. Dimitrova, G. Patlevisz, J. Niemela and O. Mekenyan, J. Chem. Inf. Model. 45 (2005)  839-849.

Daphnia magna EC50 24h

Model Features