The importance of marine alga, namely Dunaliella tertiolecta, in toxicity
determination of organics, inorganics, and mixtures, as well as for raw and treated
industrial effluents is emphasized. Ultrastructural changes for metals below the toxic
concentration in D. tertiolecta are also highlighted. Examples for synergistic,
antagonistic and hormetic effects in case of exposure of D. tertiolecta to chemicals are
given. In a case study, we focus on modeling the toxicity of selected phenols to D.
tertiolecta. Quantitative structure-activity relationship (QSAR) methodology is
employed to model the toxicities of phenolic compounds to D. tertiolecta using counterpropagation
artificial neural networks (CP ANN). The endpoint for the toxicity
determination is growth inhibition of algae exposed to chemicals in a batch system
containing natural sea water enriched by the modified f/2 medium. Results reveal that
QSAR methodology can be successfully applied to fill the data gap present in marine
algal ecotoxicity data.
Keywords: Antagonistic, artificial neural network, counter-propagation,
Dunaliella tertiolecta, hormetic exposure effects, in silico modelling, in vivo
toxicity, industrial effluents, marine alga, mechanistic interpretation, molecular
structure descriptors, non-linear model, phenols, QSAR, synergistic.
