The potential to use QSAR to populate ecotoxicity characterisation factors for simplified LCIA and chemical prioritisation

Purpose

Today’s chemical society use and emit an enormous number of different, potentially ecotoxic, chemicals to the environment. The vast majority of substances do not have characterisation factors describing their ecotoxicity potential. A first stage, high throughput, screening tool is needed for prioritisation of which substances need further measures.

Methods

USEtox characterisation factors were calculated in this work based on data generated by quantitative structure-activity relationship (QSAR) models to expand substance coverage where characterisation factors were missing. Existing QSAR models for physico-chemical data and ecotoxicity were used, and to further fill data gaps, an algae QSAR model was developed. The existing USEtox characterisation factors were used as reference to evaluate the impact from the use of QSARs to generate input data to USEtox, with focus on ecotoxicity data. An inventory of chemicals that make up the Swedish societal stock of plastic additives, and their associated predicted emissions, was used as a case study to rank chemicals according to their ecotoxicity potential.

Results and discussion

For the 210 chemicals in the inventory, only 41 had characterisation factors in the USEtox database. With the use of QSAR generated substance data, an additional 89 characterisation factors could be calculated, substantially improving substance coverage in the ranking. The choice of QSAR model was shown to be important for the reliability of the results, but also with the best correlated model results, the discrepancies between characterisation factors based on estimated data and experimental data were very large.

Conclusions

The use of QSAR estimated data as basis for calculation of characterisation factors, and the further use of those factors for ranking based on ecotoxicity potential, was assessed as a feasible way to gather substance data for large datasets. However, further research and development of the guidance on how to make use of estimated data is needed to achieve improvement of the accuracy of the results.

     

Immunocytochemical technique for protein localization in sections of plant tissues

There is a growing demand for methods that allow rapid and reliable in situ localization of proteins in plant cells. The immunocytochemistry protocol presented here can be used routinely to observe protein localization patterns in tissue sections of various plant species. This protocol is especially suitable for plant species with more-complex tissue architecture (such as maize, Zea mays), which makes it difficult to use an easier whole-mount procedure for protein localization. To facilitate the antibody-antigen reaction, it is necessary to include a wax-embedding and tissue-sectioning step. The protocol consists of the following procedures: chemical fixation of tissue, dehydration, wax embedding, sectioning, dewaxing, rehydration, blocking and antibody incubation. The detailed protocol, recommended controls and troubleshooting are presented here, along with examples of applications.     

Uptake and localisation of small-molecule fluorescent probes in living cells: a critical appraisal of QSAR models and a case study concerning probes for DNA and RNA

Small-molecule fluorochromes are used in biology and medicine to generate informative microscopic and macroscopic images, permitting identification of cell structures, measurement of physiological/physicochemical properties, assessment of biological functions and assay of chemical components. Modes of uptake and precise intracellular localisation of a probe are typically significant factors in its successful application. These processes and localisations can be predicted using quantitative structure activity relations (QSAR) models, which correlate aspects of the physicochemical properties of the probes (expressed numerically) with the uptake/localisation. Pay-offs of such modelling include better understanding and trouble-shooting of current and novel probes, and easier design of future probes (“guided synthesis”). Uptake models discussed consider adsorptive (to lipid or protein domains), phagocytic and pinocytotic endocytosis, as well as passive diffusion. Localisation models discussed include those for cytosol, endoplasmic reticulum, Golgi apparatus, lipid droplets, lysosomes, mitochondria, nucleus and plasma membrane. A case example illustrates how such QSAR modelling of probe interactions can clarify localisation and mode of binding of probes to intracellular nucleic acids of living cells, including not only eukaryotic chromatin DNA and ribosomal RNA, but also prokaryote chromosomes.     

Identifying apoptotic cells with the 3-hydroxyflavone derivative F2N12S,a ratiometric fluorescent small molecule probe selective for plasma membranes: a possible general mechanism for selective uptake into apoptotic cells

Abstract

The mechanism of selective targeting of the plasma membrane of apoptotic cells by F2N12S, a recently reported ratiometric, fluorescent small molecule probe, was analyzed using decision-rule QSAR models. Selectivity was determined by a combination of the probe's weak amphiphilicity and slow flip-flop with the increased plasma membrane fluidity of apoptotic cells. The probable chemical features required for such probes may be defined in terms of numerical structural parameters as: 3.5 < AI < ～ 5.5; log P < 5.0; HGS > 400 (where AI, log P and HGS parameters model amphiphilicity, lipophilicity and headgroup size, respectively). When HGS is <400, compounds are initially membrane selective, but subsequently are internalized.     

Novel coumarin-6-sulfonamides as apoptotic anti-proliferative agents: synthesis,in vitro biological evaluation,and QSAR studies

Herein, we report the synthesis of different novel sets of coumarin-6-sulfonamide derivatives bearing different functionalities (4a, b, 8a–d, 11a–d, 13a, b, and 15a–c), and in vitro evaluation of their growth inhibitory activity towards the proliferation of three cancer cell lines; HepG2 (hepatocellular carcinoma), MCF-7 (breast cancer), and Caco-2 (colon cancer). HepG2 cells were the most sensitive cells to the influence of the target coumarins. Compounds 13a and 15a emerged as the most active members against HepG2 cells (IC₅₀?=?3.48?±?0.28 and 5.03?±?0.39?µM, respectively). Compounds 13a and 15a were able to induce apoptosis in HepG2 cells, as assured by the upregulation of the Bax and downregulation of the Bcl-2, besides boosting caspase-3 levels. Besides, compound 13a induced a significant increase in the percentage of cells at Pre-G1 by 6.4-folds, with concurrent significant arrest in the G2-M phase by 5.4-folds compared to control. Also, 13a displayed significant increase in the percentage of annexin V-FITC positive apoptotic cells from 1.75–13.76%. Moreover, QSAR models were established to explore the structural requirements controlling the anti-proliferative activities.     

QSAR studies on the human sirtuin 2 inhibition by non-covalent 7,5,2-anilinobenzamide derivatives

Abstract

Sirtuin 2 is a key enzyme in gene expression regulation that is often associated with tumor proliferation control and therefore is a relevant anticancer drug target. Anilinobenzamide derivatives have been discussed as selective sirtuin 2 inhibitors and can be developed further. In the present study, hologram and three-dimensional quantitative structure–activity relationship (HQSAR and 3D-QSAR) analyses were employed for determining structural contributions of a compound series containing human sirtuin-2-selective inhibitors that were then correlated with structural data from the literature. The final QSAR models were robust and predictive according to statistical validation (q² and r²_pred values higher than 0.85 and 0.75, respectively) and could be employed further to generate fragment contribution and contour maps. 3D-QSAR models together with information about the chemical properties of sirtuin 2 inhibitors can be useful for designing novel bioactive ligands.

Communicated by Ramaswamy H. Sarma     

Identification of novel 5-hydroxy-1<Emphasis Type="Italic">H</Emphasis>-indole-3-carboxylates with anti-HBV activities based on 3D QSAR studies

Infection with hepatitis B virus (HBV) is a major cause of liver diseases such as cirrhosis and hepatocellular carcinoma. In our previous studies, we identified indole derivatives that have anti-HBV activities. In this study, we optimize a series of 5-hydroxy-1H-indole-3-carboxylates, which exhibited potent anti-HBV activities, using three-dimensional quantitative structure-activity relationship (3D QSAR) studies with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The lowest energy conformation of compound 3, which exhibited the most potent anti-HBV activity, obtained from systematic search was used as the template for alignment. The best predictions were obtained with the CoMFA standard model (q ² = 0.689, r ² = 0.965, SEE = 0.082, F = 148.751) and with CoMSIA combined steric, electrostatic, hydrophobic and H-bond acceptor fields (q ² = 0.578, r ² = 0.973, SEE = 0.078, F = 100.342). Both models were validated by an external test set of six compounds giving satisfactory prediction. Based on the clues derived from CoMFA and CoMSIA models and their contour maps, another three compounds were designed and synthesized. Pharmacological assay demonstrated that the newly synthesized compounds possessed more potent anti-HBV activities than before (IC₅₀: compound 35a is 3.1 μmol/l, compound 3 is 4.1 μmol/l). Combining the clues derived from the 3D QSAR studies and from further validation of the 3D QSAR models, the activities of the newly synthesized indole derivatives were well accounted for. Furthermore, this showed that the CoMFA and CoMSIA models proved to have good predictive ability.     

Binding affinity prediction of novel estrogen receptor ligands using receptor-based 3-D QSAR methods

We have recently reported the development of a 3-D QSAR model for estrogen receptor ligands showing a significant correlation between calculated molecular interaction fields and experimentally measured binding affinity. The ligand alignment obtained from docking simulations was taken as basis for a comparative field analysis applying the GRID/GOLPE program. Using the interaction field derived with a water probe and applying the smart region definition (SRD) variable selection procedure, a significant and robust model was obtained (q²_LOO=0.921, SDEP=0.345). To further analyze the robustness and the predictivity of the established model several recently developed estrogen receptor ligands were selected as external test set. An excellent agreement between predicted and experimental binding data was obtained indicated by an external SDEP of 0.531. Two other traditionally used prediction techniques were applied in order to check the performance of the receptor-based 3-D QSAR procedure. The interaction energies calculated on the basis of receptor–ligand complexes were correlated with experimentally observed affinities. Also ligand-based 3-D QSAR models were generated using program FlexS. The interaction energy-based model, as well as the ligand-based 3-D QSAR models yielded models with lower predictivity. The comparison with the interaction energy-based model and with the ligand-based 3-D QSAR models, respectively, indicates that the combination of receptor-based and 3-D QSAR methods is able to improve the quality of prediction.