ABSTRACT
Xenobiotics released in the environment can be taken up by aquatic and terrestrial organisms and can accumulate at higher concentrations through the trophic chain. Bioaccumulation is therefore one of the PBT properties that authorities require to assess for the evaluation of the risks that chemicals may pose to humans and the environment. The use of an integrated testing strategy (ITS) and the use of multiple sources of information are strongly encouraged by authorities in order to maximize the information available and reduce testing costs. Moreover, considering the increasing demand for development and the application of new approaches and alternatives to animal testing, the development of in silico cost-effective tools such as QSAR models becomes increasingly important. In this study, a large and curated literature database of fish laboratory-based values of dietary biomagnification factor (BMF) was used to create externally validated QSARs. The quality categories (high, medium, low) available in the database were used to extract reliable data to train and validate the models, and to further address the uncertainty in low-quality data. This procedure was useful for highlighting problematic compounds for which additional experimental effort would be required, such as siloxanes, highly brominated and chlorinated compounds. Two models were suggested as final outputs in this study, one based on good-quality data and the other developed on a larger dataset of consistent Log BMFL values, which included lower-quality data. The models had similar predictive ability; however, the second model had a larger applicability domain. These QSARs were based on simple MLR equations that could easily be applied for the predictions of dietary BMFL in fish, and support bioaccumulation assessment procedures at the regulatory level. To ease the application and dissemination of these QSARs, they were included with technical documentation (as QMRF Reports) in the QSAR-ME Profiler software for QSAR predictions available online.
ABSTRACT
The bioconcentration factor (BCF) is one of the metrics used to evaluate the potential of a substance to bioaccumulate into aquatic organisms. In this work, linear and non-linear regression QSARs were developed for the prediction of log BCF using different computational approaches, and starting from a large and structurally heterogeneous dataset. The new MLR-OLS and ANN regression models have good fitting with R2 values of 0.62 and 0.70, respectively, and comparable external predictivity with R2ext 0.64 and 0.65 (RMSEext of 0.78 and 0.76), respectively. Furthermore, linear and non-linear classification models were developed using the regulatory threshold BCF >2000. A class balanced subset was used to develop classification models which were applied to chemicals not used to create the QSARs. These classification models are characterized by external and internal accuracy up to 84% and 90%, respectively, and sensitivity and specificity up to 90% and 80%, respectively. QSARs presented in this work are validated according to regulatory requirements and their quality is in line with other tools available for the same endpoint and dataset, with the advantage of low complexity and easy application through the software QSAR-ME Profiler. These QSARs can be used as alternatives for, or in combination with, existing models to support bioaccumulation assessment procedures.
ABSTRACT
The new software QSARINS-Chem standalone version is a multiplatform tool, freely downloadable, for the in silico profiling of multiple properties and activities of organic chemicals. This software, which is based on the concept of the QSARINS-chem module embedded in the QSARINS software, has been fully redesigned and redeveloped in the Java™ language. In addition to a selection of models included in the old module, the new software predicts biotransformation rates and aquatic toxicities of pharmaceuticals and personal care products in multiple organisms, and offers a suite of tools for the analysis of predictions. Furthermore, a comprehensive and transparent database of molecular structures is provided. The new QSARINS-Chem standalone version is an informative and solid tool, which is useful to support the assessment of the potential hazard and risks related to organic chemicals and is dedicated to users which are interested in the application of QSARs to generate reliable predictions.
Subject(s)
Organic Chemicals/chemistry , Quantitative Structure-Activity Relationship , Software , Animals , Fishes , Molecular Structure , Organic Chemicals/toxicityABSTRACT
A new class of compounds based on the 4,4-difluoro-4-bora-3a,4a-diaza-s-indacene core, known as BODIPYs, has attracted significant attention as photosensitizers suitable for application in photodynamic therapy (PDT), which is a minimally invasive procedure to treat cancer. In PDT the combination of a photosensitizer (PS), light, and oxygen leads to a series of photochemical reactions generating reactive oxygen species (ROS) exerting cytotoxic action on tumor cells. Here we present the synthesis and the study of the in vitro photodynamic effects of two BODIPYs which differ in the structure of the substituent placed on the meso (or 8) position of the dipyrrolylmethenic nucleus. The two compounds were tested on three human cancer cell lines of different origin and degree of malignancy. Our results indicate that the BODIPYs are very effective in reducing the growth/viability of HCT116, SKOV3 and MCF7 cells when irradiated with a green LED source, whereas they are practically devoid of activity in the dark. Phototoxicity occurs mainly through apoptotic cell death, however necrotic cell death also seems to play a role. Furthermore, singlet oxygen generation and induction of the increase of reactive oxygen species also appear to be involved in the photodynamic effect of the BODIPYs. Finally, it is worth noting that the two BODIPYs are also able to exert anti-migratory activity.
