The usefulness of an artificial membrane accumulation index for estimation of the bioconcentration factor of organophosphorus pesticides

Theoretical and Experimental Study of Molecular Interactions of Fluralaner with Lipid Membranes

Fluralaner is a relatively new insecticide belonging to the isoxazoline group, whose action mechanism involves the blocking of GABA_A-receptors in the insect nervous system. Because of its high hydrophobicity, fluralaner could bioaccumulate and reach toxic local concentrations. Since there are no data available about the penetration and persistence of isoxazolines in biological membranes, we intend to evaluate fluralaner permanence as a pollutant by using model membranes. We used experimental and in silico models to characterize the incorporation of fluralaner into the lipid phase at different packing states. We determined its impact in the membrane structure and organization. Our results confirm that fluralaner is capable of penetrating, holding, and accumulating in the lipid membrane and provide details on its precise location and orientation. These properties would allow fluralaner to reach high local concentrations in different membranes and organs, which could be dangerous for vertebrate organisms if its handling is not properly controlled.

In silico model-based exploration of the applicability of parallel artificial membrane permeability assay (PAMPA) to screen chemicals of environmental concern

Parallel Artificial Membrane Permeability Assay (PAMPA) is an in vitro laboratory method for screening the transmembrane permeability of chemicals. Stemming from medicinal chemistry, PAMPA has the potential for use in the cost-effective high-throughput evaluation of chemicals of environmental concern. However, many chemicals of environmental concern differ substantially from pharmaceuticals in hydrophobicity and volatility. Here, we develop an in silico mass balance model to explore the impacts of chemical properties on chemical mass transfer in PAMPA and PAMPA's applicability to hydrophobic or volatile chemicals of environmental concern. The model's performance is evaluated by agreement between predicted and measured permeabilities of 1383 chemicals. The model predicts that the PAMPA measured permeability can be highly uncertain for hydrophobic chemicals because of considerable retention by the artificial membrane and for volatile chemicals because of substantial volatilization to the headspace. Notably, the permeabilities of hydrophobic chemicals are remarkably sensitive to specific experimental conditions, for example, the frequency of stirring and incubation time, challenging the comparison between measurements under different conditions. For hydrophobic chemicals, the PAMPA measured permeability may largely indicate the permeability of the unstirred water layer over the membrane, instead of the "intrinsic" permeability of the membrane, and therefore, may not be of interest for environmental exposure and risk assessments. The model also predicts that the time for mass transfer of highly hydrophobic chemicals to reach the steady state likely exceeds the incubation time, which violates the steady-state assumption used in calculating permeability from measured concentrations. Overall, our theoretical analysis underscores the importance to consider chemical properties when applying the current design of PAMPA to chemicals of environmental concern.

In silico local QSAR modeling of bioconcentration factor of organophosphate pesticides

The persistent and accumulative nature of the pesticide of indiscriminate use emerged as ecotoxicological hazards. The bioconcentration factor (BCF) is one of the key elements for environmental assessments of the aquatic compartment. Limitations of prediction accuracy of global model facilitate the use of local predictive models in toxicity modeling of emerging compounds. The BCF data of diverse organophosphate (n = 55) was collected from the Pesticide Properties Database and used as a model data set in the present study to explore physicochemical properties and structural alert concerning BCF. The structures were downloaded from Pubchem, ChemSpider database. Two splitting techniques (biological sorting and structure-based) were used to divide the whole dataset into training and test set compounds. The QSAR study was carried out with two-dimensional descriptors (2D) calculated from PaDEL by applying genetic algorithm (GA) as chemometric tools using QSARINS software. The models were statistically robust enough both internally as well as externally (Q2: 0.709-0.722, Q2 Ext: 0.717-0.903, CCC: 0.857-0.880). Overall molecular mass, presence of fused, and heterocyclic ring with electron-withdrawing groups affect the BCF value. The developed models reflected extended applicability domain (AD) and reliable predictions than the reported models for the studied chemical class. Finally, predictions of unknown organophosphate pesticides and the toxic nature of unknown organophosphate pesticides were commented on. These findings may be useful for the scientific community in prioritizing high potential pesticides of organophosphate class.

Development of liposome/water partition coefficients predictive models for neutral and ionogenic organic chemicals

Membrane/water partition coefficient (K_m/w) is a vital parameter used to characterize the membrane permeability of compounds. Considering the K_m/w value is difficult to observe experimentally for real biological membranes, liposome/water partition coefficient (K_lip/w) is employed to approximate K_m/w. Here, quantitative structure property relationship (QSPR) models for logK_lip/w of the neutral organic chemicals and the neutral form of ionogenic organic chemicals (IOCs) (logK_{lip/w-neutral}), ionic form of IOCs (logK_lip/w-ionic), the speciation-corrected liposome-water distribution ratios at a pH = 7.40 (logD_{lip/w-(pH=7.40)}) were developed. In the modeling, two modeling methods (multiple linear regressions (MLR) and k-nearest neighbor (kNN)) were used. The predictive variables employed here could be calculated from the molecular structure directly. For logK_{lip/w-neutral} and logD_{lip/w-(pH=7.40)}, the logK_OW and logD_OW-based, non-logK_OW and non-logD_OW-based kNN-QSPR and MLR-QSPR models were developed, respectively. The evaluation results implied that the predictive performance of kNN-QSPR models is better than that of MLR-QSPR models. For logK_lip/w-ionic, only one acceptable MLR-QSPR model was developed for cation and anion, respectively. The model quality of the derived models was evaluated following the OECD QSPR models validation guideline. The determination coefficient (R²), leave-one-out cross validation Q² (Q²_LOO) and bootstrapping coefficient (Q²_BOOT), the external validation coefficient (Q²_EXT) of all the models met the acceptable criteria (Q² > 0.600, R² > 0.700); while the root-mean-square error (RMSE) range from 0.351 to 0.857. All the results implied that the models had good goodness-of-fit, robustness and predictive ability. Therefore, the developed models could be used to fill the data gap for substances within the applicability domain on their missing logK_{lip/w-neutral}, logK_lip/w-ionic, logD_{lip/w-(pH=7.40)} values.

Phospholipid Levels Predict the Tissue Distribution of Poly- and Perfluoroalkyl Substances in a Marine Mammal

Exposure to poly- and perfluoroalkyl substances (PFASs) has been linked to many negative health impacts in humans and wildlife. Unlike neutral hydrophobic organic pollutants, many PFASs are ionic and have been hypothesized to accumulate in both phospholipids and protein-rich tissues. Here we investigate the role of phospholipids for PFAS accumulation by analyzing associations among concurrent measurements of phospholipid, total protein, total lipid and 24 PFASs in the heart, muscle, brain, kidney, liver, blubber, placenta and spleen of North Atlantic pilot whales (Globicephala melas). The sum of 24 PFASs ( ∑ 24 PFAS ) was highest in the liver (median 260 ng g^-1; interquartile range (IQR) 216-295 ng g^-1) and brain (86.0; IQR 54.5-91.3 ng g^-1), while phospholipid levels were highest in brain. The relative abundance of PFASs in the brain greatly increases with carbon chain lengths of 10 or greater, suggesting shorter-chained compounds may cross the blood-brain barrier less efficiently. Phospholipids were significant predictors of the tissue distribution of the longest-chained PFASs: perfluorodecanesulfonate (PFDS), perfluorododecanoate (PFDoA), perfluorotridecanoate (PFTrA), and perfluorotetradecanoic acid (PFTA) (r_s = 0.5-0.6). In all tissues except the brain, each 1 mg g^-1 increase in phospholipids led to a 12%-25% increase in the concentration of each PFAS. We conclude that partitioning to phospholipids is an important mechanism of bioaccumulation for long-chained PFASs in marine mammals.

Membrane partitioning of ionic liquid cations, anions and ion pairs - Estimating the bioconcentration potential of organic ions

Recent efforts have been directed towards better understanding the persistency and toxicity of ionic liquids (ILs) in the context of the "benign-by-design" approach, but the assessment of their bioaccumulation potential remains neglected. This paper reports the experimental membrane partitioning of IL cations (imidazolium, pyridinium, pyrrolidinium, phosphonium), anions ([C(CN)₃]^-, [B(CN)₄]^-, [FSO₂)₂N]^-, [(C₂F₅)₃PF₃]^-, [(CF₃SO₂)₂N]^-) and their combinations as a measure for estimating the bioconcentration factor (BCF). Both cations and anions can have a strong affinity for phosphatidylcholine bilayers, which is mainly driven by the hydrophobicity of the ions. This affinity is often reflected in the ecotoxicological impact. Our data revealed that the bioconcentration potential of IL cations and anions is much higher than expected from octanol-water-partitioning based estimations that have recently been presented. For some ILs, the membrane-water partition coefficient reached levels corresponding to BCFs that might become relevant in terms of the "B" (bioaccumulation potential) classification under REACH. However, this preliminary estimation need to be confirmed by in vivo bioconcentration studies.

Linear and nonlinear models for predicting fish bioconcentration factors for pesticides

This work is devoted to the applications of the multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and projection pursuit regression (PPR) to quantitative structure-property relationship analysis of bioconcentration factors (BCFs) of pesticides tested on Bluegill (Lepomis macrochirus). Molecular descriptors of a total of 107 pesticides were calculated with the DRAGON Software and selected by inverse enhanced replacement method. Based on the selected DRAGON descriptors, a linear model was built by MLR, nonlinear models were developed using MLP NN and PPR. The robustness of the obtained models was assessed by cross-validation and external validation using test set. Outliers were also examined and deleted to improve predictive power. Comparative results revealed that PPR achieved the most accurate predictions. This study offers useful models and information for BCF prediction, risk assessment, and pesticide formulation.

Bioconcentration of pesticides in zebrafish eleutheroembryos (Danio rerio)

The feasibility of a bioaccumulation test based on the use of zebrafish eleutheroembryos as an alternative to adult-individual-based approaches for REACH application has been evaluated for three test compounds, chlorpyrifos, dicofol and atrazine. Following the OECD 305 guidelines, zebrafish eleutheroembryos (72 h after hatching, hpf) were separately exposed to the investigated pesticides at two nominal concentrations below 1% of its corresponding LC(50). The uptake experiments lasted for 48 h. Then, the exposure medium was replaced by a non-contaminated medium for depuration experiments (up to 72 h). Zebrafish eleutheroembryos (larvae 144 hpf, i.e. at the end of the depuration step) and their corresponding exposure media was sampled at ten different times during each experiment and the concentration of the investigated pesticide determined in both the organisms and in the exposure medium. The experimentally determined pesticide accumulation profiles in the eleutheroembryos demonstrated that atrazine has a very fast accumulation kinetic, reaching steady sate (SS) within 24h. Chlorpyrifos and dicofol did not reach the SS within the 48-h uptake experiments although they exhibit higher accumulations than the former pesticide. Two toxicokinetic models were used to calculate the bioconcentration factor (BCF) of the studied pesticide in zebrafish eleutheroembryos. In the former, the BCF was calculated under SS conditions (BCF(SS)). The second was used when the compounds did not reach the SS during the uptake experiment (BCF(k)). Log BCF values of 3.55 and 3.84 for chlorpyrifos; 0.6 and 1.17 for atrazine, and 3.90 for dicofol were experimentally calculated at selected exposure concentrations. These values have been compared with those reported in related bioaccumulation studies and official databases.

Importance of physicochemical properties for the design of new pesticides

The physicochemical properties of candidate compounds play important roles in the design of new pesticides. Pesticides must be absorbed by pests, be transported to the target site, and then interact with proteins. Hydrophobicity is very important for these processes. Log P, where P is the partition coefficient in the 1-octanol/water system, is commonly used as a hydrophobic descriptor and correlates with membrane permeation and transport. It was recently reported that permeability by the parallel artificial membrane permeation assay (PAMPA) could be used to predict human oral absorption of passively transported compounds. PAMPA, which is a rapid high-throughput screening system, may be useful to predict pesticide absorption because PAMPA permeability can be calculated using log P and other parameters. Electronic and structural properties as well as hydrophobicity are important factors for protein-ligand interaction. To show the importance of physicochemical properties, the classic QSAR and CoMFA of neonicotinoids and prediction of bioavailability of pesticides in terms of membrane permeability in comparison with drugs are described.

QSAR model for the prediction of bio-concentration factor using aqueous solubility and descriptors considering various electronic effects

The in silico modelling of bio-concentration factor (BCF) is of considerable interest in environmental sciences, because it is an accepted indicator for the accumulation potential of chemicals in organisms. Numerous QSAR models have been developed for the BCF, and the majority utilize the octanol/water partition coefficient (log P) to account for the penetration characteristics of the chemicals. The present work used descriptors from a variety of software packages for the development of a multi-linear regression model to estimate BCF. The modelled data set of 473 diverse compounds covers a wide range of log BCF values. In the proposed QSAR model, most of the variation is described by the calculated solubility in water. Other contributing descriptors describe, for instance, hydrophobic surface area, hydrogen bonding and other electronic effects. The model was validated internally by using a variety of statistical approaches. Two external validations were also performed. For the former validation, a subset from the same data source was used. The 2nd external validation was based on an independent data set collected from different resources. All validations showed the consistency of the model. The applicability domain of the model was discussed and described and a thorough outlier analysis was performed.

In silico prediction of human oral absorption based on QSAR analyses of PAMPA permeability

The parallel artificial membrane permeation assay (PAMPA) was developed as a model for the prediction of transcellular permeation in the process of drug absorption. Our research group has measured the PAMPA permeability of peptide-related compounds, diverse drugs, and agrochemicals. This work led to a classical quantitative structure-activity relationship (QSAR) equation for PAMPA permeability coefficients of structurally diverse compounds based on simple physicochemical parameters such as lipophilicity at a particular pH (log P(oct) and |pKa-pH|), H-bond acceptor ability (SA(HA)), and H-bond donor ability (SA(HD)). Since the PAMPA permeability of lipophilic compounds decreased with their apparent lipophilicity due to the unstirred water layer (UWL) barrier on membrane surfaces and to membrane retention, a bilinear QSAR model was introduced to explain the permeability of a broader set of compounds using the same physicochemical parameters as those used for the linear model. We also compared PAMPA and Caco-2 cell permeability coefficients of compounds transported by various absorption mechanisms. The compounds were classified according to their absorption pathway (passively transported compounds, actively transported compounds, and compounds excreted by efflux systems) in the plot of Caco-2 vs. PAMPA permeability. Finally, based on the QSAR analyses of PAMPA permeability, an in silico prediction model of human oral absorption for possibly transported compounds was proposed, and the usefulness of the model was examined.

Determination of permeability and lipophilicity of pyrazolo-pyrimidine tyrosine kinase inhibitors and correlation with biological data

A library of 23 pyrazolo-pyrimidine compounds Src tyrosine kinase (TK) inhibitors, that reduced proliferation of a human osteogenic sarcoma cell line, was taken to investigate lack of correlation between inhibition of cellular viability (CV%) and enzymatic inhibition constants (K(i) Src). With the aim of understanding this behaviour, we focused on physico-chemical parameters which characterize partition coefficient and diffusion through membrane. Parallel artificial membrane permeability assay (PAMPA) has been frequently used for the evaluation of in vitro permeability of new chemical entities and, in this paper, a new approach for determining permeability of low soluble compounds was obtained. Goodness of PAMPA methodology was confirmed by logK(w) and computational approaches, by VolSurf, Cerius(2) and QikProp software programs. The results suggest that the lipophilicity and passive diffusion across the membranes do not significantly influence the activity of the compounds. This trend can be explained by a different target for some of the compounds in our set. In fact some compounds resulted also to be active toward Abl enzyme, another cytoplasmatic TK.