Evaluation of toxic effects of several carboxylic acids on bacterial growth by toxicodynamic modelling

Abstract

BackgroundEffects of organic acids on microbial fermentation are commonly tested in investigations about metabolic behaviour of bacteria. However, they typically provide only descriptive information without modelling the influence of acid concentrations on bacterial kinetics.ResultsWe developed and applied a mathematical model (secondary model) to capture the toxicological effects of those chemicals on kinetic parameters that define the growth of bacteria in batch cultures. Thus, dose-response kinetics were performed with different bacteria (Leuconostoc mesenteroides, Carnobacterium pisicola, Escherichia coli, Bacillus subtilis and Listonella anguillarum) exposed at increasing concentrations of individual carboxylic acids (formic, acetic, propionic, butyric and lactic). In all bioassays the acids affected the maximum bacterial load (Xm) and the maximum growth rate (vm) but only in specific cases the lag phase (λ) was modified. Significance of the parameters was always high and in all fermentations the toxicodynamic equation was statistically consistent and had good predictability. The differences between D and L-lactic acid effects were significant for the growth of E. coli, L. mesenteroides and C. piscicola. In addition, a global parameter (EC50,τ) was used to compare toxic effects and provided a realistic characterization of antimicrobial agents using a single value.ConclusionsThe effect of several organic acids on the growth of different bacteria was accurately studied and perfectly characterized by a bivariate equation which combines the basis of dose-response theory with microbial growth kinetics (secondary model). The toxicity of carboxylic acids was lower with the increase of the molecular weight of these chemicals.

Toxicity of four spill-treating agents on bacterial growth and sea urchin embryogenesis

“…The bacteria used in the current study were selected because of its different habitats (marine and terrestrial) and cell wall structure (Gram-positive and Gram-negative) characteristics. Furthermore, they previously showed excellent results as target microorganisms for toxicological evaluation of organic acids (Vázquez, et al, 2011) and heavy metals (Rial et al, 2011). Phaeobacter sp.…”

“…It also has greater ecological relevance than the assessment endpoint most common for this trophic level, namely the production of bioluminescence by Vibrio fischeri (ISO, 2007), but presents lower sensitivity (Gellert, 2000). The ISO 10712 (ISO, 1995) provides a practical alternative to evaluate the inhibitory effects of a toxic agent on the growth of Pseudomonas putida, though a more comprehensive description of the kinetics is obtained using a bivariate model which combines dose of the agent and exposure time (Rial, et al, 2011;Vázquez, et al, 2011). Bioassays based on the success of the embryo-larval development of marine invertebrates are tools commonly used in ecotoxicology because of its simplicity, low cost and high sensitivity (His, et al, 2000).…”

Re-evaluation of polihexanide use in wound antisepsis in order to clarify ambiguities of two animal studies

“…43 For other antiseptic agents, an intracellular effect is only relevant for iodophors and certain, specially formulated and prepared mixtures of peroxide and carboxylic acid. 44 PHMB is not only effective against planktonic but also against sessile polymicrobial communities. Microorganisms are eliminated in biofilms in vitro as effectively as by CHG. 45 PHMB also reduces biofilms in 3D skin models 46 and in porcine wounds.…”

Effects of spill-treating agents on growth kinetics of marine microalgae

“…The bivariate model is based on a reparameterised logistic equation to describe growth combined with a Weibull function to describe concentration-dependent variations in growth parameters [9,12]:…”

“…Recently, a bivariate model was developed to evaluate toxic effects on growth kinetics by combining the logistic equation to describe microbial growth and a cumulative Weibull function of the dose-response relations of the parameters of the growth curve [9,12]. This model allows a comprehensive description of inhibitory effects in the experimental domain of concentration and time.…”