Effects of active pharmaceutical ingredients in mussel mytilus galloprovincialis

More than 3000 types of active pharmaceutical ingredients (APIs) are applied in Human and veterinary medicine practice. These compounds are considered an emergent class of environmental contaminants with the ability to cause damage and unexpected effects to aquatic organisms, namely in species of high commercial value. APIs are ubiquitous in the environment being frequently detected in influents and effluents of waste water treatment plants (WWTPs), surface waters and more distressingly in the public tap water in concentrations ranging from ng to μg.L-1. Considering these premises, the present thesis focused on APIs detection in the Arade river water, the impact of summer period in APIs’ concentration alterations applying the passive sampler device, POCIS (polar organic compound integrative sampler), as well as, the assessment of the effects caused by non-steroidal anti-inflammatory drugs (NSAID) ibuprofen (IBU) and diclofenac (DCF) and antidepressant selective serotonin reuptake inhibitor (SSRI) fluoxetine as single and mixture exposures along with a classical contaminant copper (Cu) on a non-target species, mussel Mytilus galloprovincialis. For this purpose, a multibiomarker approach was applied namely including biomarkers of oxidative stress (antioxidant enzymes activities of superoxide dismutase – SOD, catalase – CAT, glutathione reductase – GR and Phase II glutathione-S-transferase), damage - lipid peroxidation (LPO), neurotoxic effects (through the activity of acetylcholinesterase enzyme - AChE) and endocrine disruption (through vitellogenin-like proteins measurement applying the indirect method of alkali-labile phosphate - ALP) after exposure of mussel species’ to selected APIs at environmental relevant concentrations. The main results highlighted the occurrence of 19 APIs in the river Arade from several distinct therapeutic classes. Stimulant caffeine, antiasthmatic theophylline, NSAID ibuprofen and analgesic paracetamol presented the highest concentrations. Summer impact was inconclusive due to each API transient concentration in each month. The multibiomarker results revealed distinct responses towards each selected API (as single exposure or as mixtures) that were tissue and time dependent. Several multistressor interactions were proposed for each biomarker. The results also revealed APIs potential to induce oxidative stress, LPO, neurotoxicity and endocrine disruption even at extremely low concentrations on a species extremely vulnerable to APIs presence highlighting the urgency on the development of methodologies able to prevent its entrance in the aquatic environment.

An alternative methodology for fitting selectivity curves to pre-defined distributions

A non-linear least-squares methodology for simultaneously estimating parameters of selectivity curves with a pre-defined functional form, across size classes and mesh sizes, using catch size frequency distributions, was developed based on the model of Kirkwood and Walker [Kirkwood, G.P., Walker, T.L, 1986. Gill net selectivities for gummy shark, Mustelus antarcticus Gunther, taken in south-eastern Australian waters. Aust. J. Mar. Freshw. Res. 37, 689-697] and [Wulff, A., 1986. Mathematical model for selectivity of gill nets. Arch. Fish Wiss. 37, 101-106]. Observed catches of fish of size class I in mesh m are modeled as a function of the estimated numbers of fish of that size class in the population and the corresponding selectivities. A comparison was made with the maximum likelihood methodology of [Kirkwood, G.P., Walker, T.I., 1986. Gill net selectivities for gummy shark, Mustelus antarcticus Gunther, taken in south-eastern Australian waters. Aust. J. Mar. Freshw. Res. 37, 689-697] and [Wulff, A., 1986. Mathematical model for selectivity of gill nets. Arch. Fish Wiss; 37, 101-106], using simulated catch data with known selectivity curve parameters, and two published data sets. The estimated parameters and selectivity curves were generally consistent for both methods, with smaller standard errors for parameters estimated by non-linear least-squares. The proposed methodology is a useful and accessible alternative which can be used to model selectivity in situations where the parameters of a pre-defined model can be assumed to be functions of gear size; facilitating statistical evaluation of different models and of goodness of fit. (C) 1998 Elsevier Science B.V.