Toxicity assessment of organic pollutants: Reliability of bioluminescence inhibition assay and univariate QSAR models using freshly prepared Vibrio fischeri

Parvez, Shahid; Venkataraman, Chandra; Mukherji, Suparna

doi:10.1016/j.tiv.2008.07.011

Cited by 31 publications

(17 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This test provides a measure of nonspecific toxicity. Toxicity is found to increase with the increase in hydrophobicity which is commonly indicated by the octanol water partition coefficient, K ow [89]. However, for surfactants, other measures of hydrophobicity provide better correlation.…”

Section: Surfactant Aided Biodegradation Of Petroleum Hydrocarbonsmentioning

confidence: 99%

Practical Considerations and Challenges Involved in Surfactant Enhanced Bioremediation of Oil

Mohanty

Jasmine

Mukherji

2013

BioMed Research International

Self Cite

View full text Add to dashboard Cite

Surfactant enhanced bioremediation (SEB) of oil is an approach adopted to overcome the bioavailability constraints encountered in biotransformation of nonaqueous phase liquid (NAPL) pollutants. Fuel oils contain n-alkanes and other aliphatic hydrocarbons, monoaromatics, and polynuclear aromatic hydrocarbons (PAHs). Although hydrocarbon degrading cultures are abundant in nature, complete biodegradation of oil is rarely achieved even under favorable environmental conditions due to the structural complexity of oil and culture specificities. Moreover, the interaction among cultures in a consortium, substrate interaction effects during the degradation and ability of specific cultures to alter the bioavailability of oil invariably affect the process. Although SEB has the potential to increase the degradation rate of oil and its constituents, there are numerous challenges in the successful application of this technology. Success is dependent on the choice of appropriate surfactant type and dose since the surfactant-hydrocarbon-microorganism interaction may be unique to each scenario. Surfactants not only enhance the uptake of constituents through micellar solubilization and emulsification but can also alter microbial cell surface characteristics. Moreover, hydrocarbons partitioned in micelles may not be readily bioavailable depending on the microorganism-surfactant interactions. Surfactant toxicity and inherent biodegradability of surfactants may pose additional challenges as discussed in this review.

show abstract

Section: Surfactant Aided Biodegradation Of Petroleum Hydrocarbonsmentioning

confidence: 99%

Practical Considerations and Challenges Involved in Surfactant Enhanced Bioremediation of Oil

Mohanty

Jasmine

Mukherji

2013

BioMed Research International

Self Cite

View full text Add to dashboard Cite

show abstract

“…Each combination was characterized by a unique concentration ratio of the various components (Berthouex and Brown, 2002;Parvez, 2008). The 11 sub-mixtures, i.e., 6-binary, 4-ternary and 1-quaternary sub-mixture, resulted in a total of 72 combinations (R (number of sub-mixtures Â number of combination in each sub-mixture) = 6 Â 4 + 4 Â 8 + 1 Â 16) for each mix- The dose-response profile of each component and mixture toxicity were obtained using the V. fischeri bioluminescence inhibition assay based on strain NRRL B-11177 with 30 min exposure time (ISO, 1998;Parvez et al, 2008b). V. fischeri grown for 24 h was used to prepare a reagent containing the harvested cells in a protective medium which could be preserved at À20°C.…”

Section: Full Factorial Design and Mixtures Toxicity Measurementmentioning

confidence: 99%

“…There is sufficient literature correlating the toxicity due to single chemicals with their physicochemical properties (Parvez et al, 2008b), however, literature on mechanisms pertaining to mixture toxicity is lacking for mixtures of hydrophobic organic compounds. The interaction between mixture components is a complex phenomenon which can not be explained by any single chemical property or structural information.…”

Section: Hypothetical Mixture-ementioning

confidence: 99%

Nature and prevalence of non-additive toxic effects in industrially relevant mixtures of organic chemicals

2009

Self Cite

View full text Add to dashboard Cite

“…Some studies have been performed on the relationship between toxicity and chemical structure of several compounds, and QSARs models have been developed to predict V. fischeri toxicity for specific groups using molecular and physicochemical descriptors [18][19][20][21][22][23]. The toxicity of narcotic compounds against V. fischeri was predicted using molecular connectivity indices (topological descriptors), and the data obtained suggested that the degree of branching and the compounds' electronic characteristics have a dominant role in the toxicity level [20].…”

Section: Introductionmentioning

confidence: 99%

“…The toxicity of narcotic compounds against V. fischeri was predicted using molecular connectivity indices (topological descriptors), and the data obtained suggested that the degree of branching and the compounds' electronic characteristics have a dominant role in the toxicity level [20]. Topological, electronic, and log P descriptors have also been used to predict the toxicity of organic pollutants against V. fischeri [19]. Charge distribution (e.g., maximum partial charge for a carbon (C) atom (Zefirov's partial charge (PC)) and geometric (e.g., shadow parameter) descriptors were used by Couling et al [21] to assess the toxicity of a diversity of ionic liquids against V. fischeri and Daphnia magna.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Terpenoid Toxicity Based on a Quantitative Structure–Activity Relationship Model

Perestrelo

Silva

Fernandes

2019

Foods

View full text Add to dashboard Cite

Terpenoids, including monoterpenoids (C 10 ), norisoprenoids (C 13 ), and sesquiterpenoids (C 15 ), constitute a large group of plant-derived naturally occurring secondary metabolites with highly diverse chemical structures. A quantitative structure-activity relationship (QSAR) model to predict terpenoid toxicity and to evaluate the influence of their chemical structures was developed in this study by assessing in real time the toxicity of 27 terpenoid standards using the Gram-negative bioluminescent Vibrio fischeri. Under the test conditions, at a concentration of 1 µM, the terpenoids showed a toxicity level lower than 5%, with the exception of geraniol, citral, (S)-citronellal, geranic acid, (±)-α-terpinyl acetate, and geranyl acetone. Moreover, the standards tested displayed a toxicity level higher than 30% at concentrations of 50-100 µM, with the exception of (+)-valencene, eucalyptol, (+)-borneol, guaiazulene, β-caryophellene, and linalool oxide. Regarding the functional group, terpenoid toxicity was observed in the following order: alcohol > aldehyde~ketone > ester > hydrocarbons. The CODESSA software was employed to develop QSAR models based on the correlation of terpenoid toxicity and a pool of descriptors related to each chemical structure. The QSAR models, based on t-test values, showed that terpenoid toxicity was mainly attributed to geometric (e.g., asphericity) and electronic (e.g., maximum partial charge for a carbon (C) atom (Zefirov's partial charge (PC)) descriptors. Statistically, the most significant overall correlation was the four-parameter equation with a training coefficient and test coefficient correlation higher than 0.810 and 0.535, respectively, and a square coefficient of cross-validation (Q 2 ) higher than 0.689. According to the obtained data, the QSAR models are suitable and rapid tools to predict terpenoid toxicity in a diversity of food products.

show abstract

Toxicity assessment of organic pollutants: Reliability of bioluminescence inhibition assay and univariate QSAR models using freshly prepared Vibrio fischeri

Cited by 31 publications

References 30 publications

Practical Considerations and Challenges Involved in Surfactant Enhanced Bioremediation of Oil

Practical Considerations and Challenges Involved in Surfactant Enhanced Bioremediation of Oil

Nature and prevalence of non-additive toxic effects in industrially relevant mixtures of organic chemicals

Prediction of Terpenoid Toxicity Based on a Quantitative Structure–Activity Relationship Model

Contact Info

Product

Resources

About