A novel QSAR approach for estimating toxicity of phenols

An Electronic-structure Informatics Study on the Toxicity of Alkylphenols to Tetrahymena pyriformis

Alkylphenols (APs) dissolved in water are known to be toxic to animals including humans. In this study, regression models describing the toxicity of the 33 AP molecules were investigated for reproducing and thereby making it possible to predict a quantitative structure-activity relationship (QSAR). For this purpose, we tried to derive regression models for the experimental IGC₅₀ (growth inhibition concentration at 50 %) to Tetrahymena pyriformis by using various descriptor sets consisting of electronic and shape descriptors. By applying the multiple linear regression (MLR) analysis, it was successful to derive a quantitative regression model for the IGC₅₀ values. In this analysis, the size parameters of the molecules were found important, suggesting that bulky molecules should be less toxic. We could also obtain, when the size descriptors were excluded, an MLR model indicating that the electron affinity (EA) should be important, which is consistent with the previous QSAR studies. Through the correlation analysis among the descriptors, it was shown that, in the present set of molecules, EA and a size parameter are highly correlated. Since EA was calculated to be negative, indicating that the related process would be energetically unfavourable, it was concluded that the size of the molecules should be a dominant factor determining IGC₅₀ . This implies that a molecular recognition process would play a critical role in the mode of action for the toxicity.

A systematic study on photocatalysis of antipyrine: Catalyst characterization, parameter optimization, reaction mechanism a toxicity evolution to plankton

The toxicity of antipyrine (AP) in the photodegradation using UV/CoFe₂O₄/TiO₂ was investigated by analyzing the characteristic of the catalyst, the effect of parameters (light source wavelength, catalyst dose, pH and initial AP concentration), the reaction mechanism (the organic intermediates, TOC reduction and inorganic ions release) and the newly proposed low-dosage-high-effective radical reaction approach. The catalyst shows the optimal removal efficiency under the conditions of wavelength at 350 nm, the catalyst dose at 0.5 g/L, and pH value at 5.5. Ten organic intermediates were identified, and five of them were newly reported in AP treatment process. Hydroxylation, demethylation and the cleavage of the pentacyclic ring were included in the decomposition pathways. The ring opening was certified by the 45% TOC reduction and 60% ammonia release during the process. The parent compound AP and its degradation products show positive effects on the growth of the algae. However, acute toxicity of AP was detected on brine shrimps Artemia salina. The toxicity was eliminated gradually with the decomposition of AP and the generation of the byproducts. The results indicate that the photocatalysis process is effective in AP removal, TOC reduction and toxicity elimination.

From data point timelines to a well curated data set, data mining of experimental data and chemical structure data from scientific articles, problems and possible solutions

The scientific literature is important source of experimental and chemical structure data. Very often this data has been harvested into smaller or bigger data collections leaving the data quality and curation issues on shoulders of users. The current research presents a systematic and reproducible workflow for collecting series of data points from scientific literature and assembling a database that is suitable for the purposes of high quality modelling and decision support. The quality assurance aspect of the workflow is concerned with the curation of both chemical structures and associated toxicity values at (1) single data point level and (2) collection of data points level. The assembly of a database employs a novel "timeline" approach. The workflow is implemented as a software solution and its applicability is demonstrated on the example of the Tetrahymena pyriformis acute aquatic toxicity endpoint. A literature collection of 86 primary publications for T. pyriformis was found to contain 2,072 chemical compounds and 2,498 unique toxicity values, which divide into 2,440 numerical and 58 textual values. Every chemical compound was assigned to a preferred toxicity value. Examples for most common chemical and toxicological data curation scenarios are discussed.

Comparison of Tetrahymena pyriformis toxicity based on hydrophobicity, polarity, ionization and reactivity of class-based compounds

A toxicity data set containing the toxicities of 970 hydrophobic, polar and ionizable, nitro substituted and α,β-unsaturated compounds to Tetrahymena pyriformis was classified into different groups based on the structure and substituted functional groups. Polar, ionizable and reactive compounds exhibit greater toxicity as compared with the non-polar hydrophobic compounds. Step-by-step analysis was carried out between the toxicity and descriptors representing hydrophobicity, polarity/polarizability, ionization and reactivity of compounds. Significant relationships were developed between the toxicity and these descriptors for the compounds. The models developed are simple, interpretable and transparent, using a small number of descriptors that may reflect the interactions of chemicals with the biological macromolecules at the target sites. Hydrophobic parameter log P reflects bio-uptake process compounds. Polarity/polarizability descriptor S reflects the interaction of hydrophilic residues of polar chemicals with biological macromolecules. The fractions of ionized (F (i)) and neutral (F (0)) forms calculated from pK (a) reflect the interactions of ionizable compounds with the macromolecules and effect of ionization of ionizable compounds on the bio-uptake process, respectively. A successful single model was developed by using the descriptors log P, S, F (i) and log F (0) for non-polar, polar and ionizable compounds.

QSTR with extended topochemical atom (ETA) indices. 14. QSAR modeling of toxicity of aromatic aldehydes to Tetrahymena pyriformis

Aldehydes are a toxic class of chemicals causing severe health hazards. In this background, quantitative structure-toxicity relationship (QSTR) models have been developed in the present study using Extended Topochemical Atom (ETA) indices for a large group of 77 aromatic aldehydes for their acute toxicity against the protozoan ciliate Tetrahymena pyriformis. The ETA models have been compared with those developed using various non-ETA topological indices. Attempt was also made to include the n-octanol/water partition coefficient (logK(o/w)) as an additional descriptor considering the importance of hydrophobicity in toxicity prediction. Thirty different models were developed using different chemometric tools. All the models have been validated using internal validation and external validation techniques. The statistical quality of the ETA models was found to be comparable to that of the non-ETA models. The ETA models have shown the important effects of steric bulk, lipophilicity, presence of electronegative atom containing substituents and functionality of the aldehydic oxygen to the toxicity of the aldehydes. The best ETA model (without using logK(o/w)) shows encouraging statistical quality (Q(int)(2)=0.709,Q(ext)(2)=0.744). It is interesting to note that some of the topological models reported here are better in statistical quality than previously reported models using quantum chemical descriptors.

Toxicity of organic pollutants to seven aquatic organisms: effect of polarity and ionization

The toxicity of organic chemicals to Vibrio fischeri, river bacteria, algae, Daphnia magna and fishes were analysed. The results showed that the toxicity of chemicals to narcotics was dependent on hydrophobicity. A single model for both polar and non-polar narcotics was developed by inclusion of a polarity descriptor as well as the hydrophobic parameter. The highly hydrophobic polar narcotics could be treated as non-polar narcotics because their polar functional group(s) make(s) a relatively small contribution to polarity as compared with their hydrophobicity. In order to investigate the toxic mechanism of action for reactive compounds, the response-surface approach was used to develop models derived from easily calculated descriptors. The stepwise analysis selected the octanol/water partition coefficient and a polarity descriptor to parameterize bio-uptake and reactivity, respectively, for seven species. Benzoic acids can be easily absorbed into the unicellular bacteria, but this is not the case for multicellular D. magna and fish. Their toxicity to V. fischeri is much higher than that to D. magna and carp. Regression analysis was performed based on the model that we developed for ionizable compounds. Good correlations were observed by introducing the correction factor for ionizable compounds. The toxic mechanisms are discussed.

Classification of toxicity of phenols to Tetrahymena pyriformis and subsequent derivation of QSARs from hydrophobic, ionization and electronic parameters

Phenolic compounds were classified into different groups based on the structure and functional groups of the phenol. Quantitative structure-activity relationship (QSAR) analysis was performed between the toxicity and octanol/water partition coefficient (logP) for these groups. The results showed that the toxicity of non-ionisable phenols is dependent on their hydrophobicity. Poor relationships were found between the toxicity and logP for ionisable compounds, and the use of methods based on logP to predict the toxicity of ionisable compounds can result in considerable errors. Ionized and unionized forms have different contributions to toxicity; the unionized form plays a more important role than the ionized form because the toxicity of organic acids and phenols decreases as the pH increases. In order to investigate the effect of ionization, the fraction of ionized and unionized forms of phenols at different pH values were calculated from the pK(a) values, and a corrected distribution partition coefficient (D(T)) was derived from QSAR analysis for ionisable compounds. The prediction of toxicity of non-reactive ionisable compounds was improved remarkably by using the D(T) parameter. Ionization not only affects the bio-uptake of ionisable compounds, but interaction with the receptor micromolecule can also depend on the electronic situation, which is also related to the ionization. Stepwise regression showed that the reactivity of ionisable phenols was strongly correlated with the fraction of negatively charged form (F(-)). Interpretable QSAR equations with good statistical fits were developed from hydrophobic, ionization and electronic parameters for 207 phenols.

Influence of phenolics on the sensitivity of free and immobilized bioluminescent Acinetobacter bacterium

In this work, the constructed bioluminescent Acinetobacter strain DF4/PUTK2 was employed to assess the toxicity of phenolic compounds and the 5 min EC50 values were calculated. The results of the DF4/PUTK2 assay were further evaluated by comparing with the results of the Vibrio fischeri luminescence inhibition assay. To develop a bioassay system appropriate to be used in continuous toxicity testing, strain DF4/PUTK2 was subjected for immobilization in microtiter plates into the matrices Ca-alginate, polyacrylamide, agar and agarose. After a choice of materials was tried, Ca-alginate was selected as the most suitable candidate material. Because, it could be stored at least 8 weeks at 4 degrees C, during which the ability of the bioreporter DF4/PUTK2 to detect the toxicity of phenolics was maintained. However, the stability of the bioluminescence for DF4/PUTK2 cells immobilized into agarose and agar was significantly less than that of cells stored in alginate suspensions. This study recommended that luxCDABE-marked Acinetobacter strain DF4/PUTK2 could be employed to assay the ecotoxicity of environmental samples contaminated with phenols. The host strain of the bioreporter DF4/PUTK2 is Acinetobacter strain DF4. It is known that members of the genus Acinetobacter are widespread in nature and also involved in biodegradation, leaching and removal of several organic and inorganic man-made hazardous wastes.

A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models

A novel automated lazy learning quantitative structure-activity relationship (ALL-QSAR) modeling approach has been developed on the basis of the lazy learning theory. The activity of a test compound is predicted from a locally weighted linear regression model using chemical descriptors and the biological activity of the training set compounds most chemically similar to this test compound. The weights with which training set compounds are included in the regression depend on the similarity of those compounds to a test compound. We have applied the ALL-QSAR method to several experimental chemical data sets including 48 anticonvulsant agents with known ED50 values, 48 dopamine D1-receptor antagonists with known competitive binding affinities (Ki), and a Tetrahymena pyriformis data set containing 250 phenolic compounds with toxicity IGC50 values. When applied to database screening, models developed for anticonvulsant agents identified several known anticonvulsant compounds that were not only absent in the training set but highly chemically dissimilar to the training set compounds. This initial success indicates that ALL-QSAR can be further exploited as a general tool for accurate bioactivity prediction and database screening in drug design and discovery. Because of its local nature, the ALL-QSAR approach appears to be especially well-suited for the development of highly predictive models for the sparse or unevenly distributed data sets.

Correlations and Predictions of pKa Values of Fluorophenols and Bromophenols Using Hydrogen-Bonded Complexes with Ammonia

Density functional theory calculations have been preformed on a series of the hydrogen-bonded fluorophenol-ammonia and bromophenol-ammonia complexes. Intermolecular and intramolecular properties, particularly those related to hydrogen bonding, have been carefully analyzed. Several properties, such as the bond length and stretching frequency of the hydroxyl group, the hydrogen bond length and binding energy, are shown to be highly correlated with each other and are linearly correlated with known experimental pKa values of the halogenated phenols. The linear correlations have been used to predict the pKa values of all fluorophenols and bromophenols in the series. The predicted pKa values are shown to be consistent from different molecular properties and are in good agreement with available experimental values. This study suggests that calculated molecular properties of hydrogen-bonded complexes allow the effective and systematic prediction of pKa values for a large range of organic acids using the established linear correlations.

Thermodynamic Descriptors Derived from Density Functional Theory Calculations in Prediction of Aquatic Toxicity

Ab initio Density Functional Theory methods at the B3LYP/6-311+G(2d,p)//HF/3-21G(d) level in the gas and solvent phases (Conductor-like Polarizable Continuum Model) were used to calculate the thermodynamic and charge related descriptors including the first-order dissociation constant for a set of 53 substituted phenols. The absolute values of the pKa DFT descriptor showed significant deviations from the experimental values of dissociation constants, but the relative trends were well-described (R2=0.929, n=40). The descriptors were used for the design of predictive models for two aquatic species Pimephales promelas and Tetrahymena pyriformis. In both cases the best performing 2-descriptor models were based on the descriptors DeltaH298gas) and ClogP and showed good predictivity with R2=0.853, R2CV=0.834 for Pimephales and R2=0.864, R2CV=0.833 for Tetrahymena. The descriptors, models and the mode of action of the compounds are discussed.

Estimation of cytotoxicity to HEP-G2 cells of 255 environmental pollutants and water using QSAR (Quantitative Structure-Activity Relationship)

Although bioassays are considered to be a rational method for environmental management, the procedure is generally too complicated to be applied to daily water quality management. In this study, the feasibility of using for application of a conventional QSAR (Quantitative Structure-Activity Relationship) method was examined to estimate the cytotoxicity of various pollutants found in environmental water. logP, pKa, and molecular weight were chosen as the physico/chemical properties of the pollutants, and defined equations for estimating cytotoxicity based on multiple linear regression analysis between these properties and in vitro cytotoxicity data from our previous results. As a result, a method for estimating cytotoxicity of environmental pollutants that had a certain probability (R>0.8) for the 255 chemicals was successfully developed. Considerably high reliability was shown in the leave-one-out prediction of multi-regression analysis. In addition, the cytotoxicity of environmental water samples was estimated based on multi-regression analysis, using as our samples leachates from 25 landfill sites in Japan. The method developed in this study estimated quantitatively the cytotoxicity of the environmental water from chemical analysis data without conducting a cytotoxicity test.

Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis

Quantitative structure-activity relationships (QSARs) for the toxicity of 200 phenols to the ciliated protozoan Tetrahymena pyriformis, and the validation of the QSARs using a test set of a further 50 compounds, are reported. The phenols are structurally heterogeneous and represent a variety of mechanisms of toxic action including polar narcosis, weak acid respiratory uncoupling, electrophilicity, and those compounds capable of being metabolised or oxidised to quinones. For each compound, a total of 108 physico-chemical descriptors have been calculated. A variety of methods were utilised to develop QSARs and are compared. The response-surface, or two parameter, approach was found to be successful, but only following the removal of compounds known to form quinones. Stepwise regression produced a seven parameter QSAR with good statistical fit, but was less interpretable and transparent than the response-surface. Partial least squares produced a good model for phenolic toxicity following supervised selection of parameters, this, however, was the least transparent of all approaches attempted. In all approaches, a large number of outliers were observed, typically these were compounds capable of being metabolised to quinones. The strengths and weaknesses of each of the approaches to predict the toxicity of the validation (test) set of phenols to T. pyriformis are discussed.

Estimating the toxicities of organic chemicals to bioluminescent bacteria and activated sludge

Toxicity assays based on bioluminescent bacteria have several advantages including a quick response and an easily measured signal. The Shk1 assay is a procedure for wastewater toxicity testing based on the bioluminescent bacterium Shk1. Using the Shk1 assay, the toxicity of 98 organic chemicals were measured and EC50 values were obtained. Quantitative structure-activity relationship (QSAR) models based on the logarithm of the octanol-water partition coefficient (log(Kow)) were developed for individual groups of organic chemicals with different functional groups. The correlation coefficients for different groups of organic compounds varied between 0.69 and 0.99. An overall QSAR model without discriminating the functional groups, which can be used for a quick estimate of the toxicities of organic chemicals, was also developed and model predictions were compared to experimental data. The model accuracy was found to be one order of magnitude from the observed values.

Classifying class I and class II compounds by hydrophobicity and hydrogen bonding descriptors

The successful development of quantitative structure-activity relationships (QSARs) and the prediction of toxicity based on QSARs depend on the correct classification of the mechanism of toxic action of chemical compounds. The toxicity mechanism of a compound can be determined by a few existing methods, such as studying the chemical structure of the compound for certain substructures and assigning a mechanism accordingly. However, these methods are less reliable for compounds with complex structures and are usually complicated. A novel descriptor-based method employing a multivariate statistical technique--discriminant analysis--was developed in this study for the classification of class I and class II compounds. The discriminating variables used in discriminant analysis were the hydrophobicity descriptor log(K(ow)) and the hydrogen bonding descriptors E(LUMO), E(HOMO), Q(+), and Q(-). Because only numerical values of these descriptors are required for this method, which can be calculated, no additional experimental work was required for the toxicity mechanism classification of new compounds. A nonlinear discriminant function was generated that could not be expressed explicitly. Assessing the predicting ability of the discriminant function by the cross-validation method showed that a low total error rate, 4.2% was achieved.

Subcellular pharmacokinetics and its potential for library focusing

Subcellular pharmacokinetics (SP) optimizes biology-related factors in the design of libraries for high throughput screening by defining comparatively narrow ranges of properties (lipophilicity, amphiphilicity, acidity, reactivity, 3D-structural features) of the included compounds. The focusing ensures appropriate absorption, distribution, metabolism, excretion, and toxicity (ADMET) in those test biosystems, which are more complex than isolated receptors, and in humans. The SP deploys conceptual models that include transport and accumulation in a series of membranes, protein binding, hydrolysis, and other reactions with cell constituents. The kinetics of drug disposition is described as a non-linear disposition function of drug structure and properties. The SP capabilities are illustrated here using a model-based quantitative structure-activity relationship of toxicity of phenolic compounds against Tetrahymena pyriformis as dependent on lipophilicity and acidity. The resulting SP models clearly outperform empirical models in predictive ability outside the parameter space, as revealed by the leave-extremes-out cross-validation technique with omission of compounds beyond pre-defined lipophilicity and acidity ranges. The SP models do not change substantially if the parameters space is shrunk within some limits. In contrast, the shapes of empirical models vary widely depending upon the fraction of the data set used for their optimization. Once calibrated for a given biosystem, the SP models provide a detailed recipe for tailoring the drug properties to ensure optimum ADMET. The focusing is more accurate than with traditional empirical QSAR studies, assessment of drug-likeness, or the rules for identification of compounds with permeability problems.

Determining the mechanisms of toxic action of phenols to Tetrahymena pyriformis

Phenols are environmentally important compounds that are widely used in agriculture and industry. Thus, phenols are of interest to environmental toxicologists, which has led to the development of several quantitative structure-activity relationship (QSAR) models. The most successful QSARs have been established for compounds having a common mechanism of toxic action (MOA). However, correctly determining the MOA of a compound is not easy. Discriminant analysis was used in this study to separate phenols by their MOAs. The working hypothesis of this study was that phenols with different MOAs lie in different regions in the response surface described by the ionization-corrected hydrophobicity descriptor, log D, the electrophilicity descriptor, ELUMO, and the interaction between hydrophobicity and electrophilicity, D * ELUMO. Results of discriminant analysis showed a total error rate of 15%. In addition, it was difficult to separate phenols that had a polar narcosis MOA from phenols that had a proelectrophile MOA.

Multiple reactive immunization towards the hydrolysis of organophosphorus nerve agents: hapten design and synthesis

We designed and synthesized a series of haptens to elicit catalytic antibodies with phosphatase activity against nerve agents. The design is based on the novel concept of multiple reactive immunization which aims to afford two or more catalytic residues within the antibody's binding cleft. The haptens showed the desired reactivity in vitro and were submitted for immunization.

QSAR analyses of the toxicity of aliphatic carboxylic acids and salts to Tetrahymena pyriformis

Carboxylic acids have been conspicuously absent from the quantitative structure activity relationship (QSAR) literature. This study investigated the aquatic toxicity (log(IGC50(-1)) of selected mono- and di-carboxylic acids and their sodium, or disodium salts, tested in the Tetrahymena population growth assay. The relationship between log(IGC50(-1)) and hydrophobicity as described by the 1-octanol/water partition coefficient (log Kow) revealed a distinct sub-class. The relationship [log(IGC50(-1)) = 0.27(log Kow) - 0.68; n = 16, r2 = 0.943, s = 0.07, F = 233, Pr > F = 0.0001] was derived for mono-carboxylic acids. The QSAR [log(IGC50(-1)) = 0.19(log Kow) - 0.66; n = 9, r2 = 0.951, s = 0.08, F = 135, Pr > F = 0.0001] was generated for the di-carboxylic acids. Regression analysis of data for the monosodium carboxylic acid salts yielded the model, log(IGC50(-1)) = 0.60 (log Kow) + 0.58; n = 4, r2 = 0.932, s = 0.19, F = 41.2, Pr > F = 0.008. Values for the ionization constant (pKa) and the energy of the lowest unoccupied molecular orbital (ELUMO) do not vary within the sub-class for saturated acids. Moreover, pKa and ELUMO did not describe differences in toxicity between sub-classes of saturated aliphatic carboxylic acids and salts. However, these descriptors did vary for unsaturated acids. Inclusion of unsaturated acids afforded the derivation of a global response-surface for all aliphatic carboxylic acids, log(IGC50(-1)) = 0.25(log Kow) - 0.13(ELUMO) - 0.54; n = 34, r2 = 0.850, s = 0.138, F = 87.9, Pr > F = 0.0001. Outliers to the response-surface included small molecules that provided 2-positions in which the molecule could potentially undergo electrophilic attacked and other more large, hydrophobic molecules.

Tetrahymena pyriformis: a tool for toxicological studies. A review

Among protozoa, Tetrahymena pyriformis is the most commonly ciliated model used for laboratory research. After a brief description of the morphology and biology of Tetrahymena pyriformis, this article focuses on the most important and recent investigations performed with this species in toxicology and ecotoxicology. The methodological features of its culture, and main tests, based on cell growth rate, biochemical markers, behavioral changes and motility, are discussed. Examples of xenobiotics (organic and inorganic substances, pharmaceutical drugs, water pollutants) tested with Tetrahymena pyriformis are also given.

QSAR study of the toxicity of benzoic acids to Vibrio fischeri, Daphnia magna and carp

The toxicities of benzoic acids to Vibrio fischeri, Daphnia magna and carp were measured. The results showed that the toxicity to V. fischeri and Daphnia decreased in the order of bromo > chloro > fluoro approximately equal to aminobenzoic acids. The toxicity of substituted benzoic acids to carp and Daphnia was much lower that to V. fischeri. The results also showed that the toxicity of benzoic acids to Daphnia decreased as the pH increased. It is suggested that ionized and non-ionized forms have different toxic responses. The non-ionized form may play an important role in toxicity because the toxicity of benzoic acids to Daphnia greatly decreases as the pH increases. The toxicity of benzoic acids to Daphnia may operate through non-polar narcosis, based on the regression results between the toxicities and partition coefficients (log P) and apparent partition coefficients (log D). However, toxicity cannot be predicted from non-polar baseline models because the ionized and non-ionized form of benzoic acids have different contributions to toxicity. Compared with the single descriptors, the prediction of toxicity of the benzoic acids was improved remarkably by using log P with pKa and log P with ELUMO. For the toxicity of benzoic acids to V. fischeri, it is suggested that the toxic mechanism may be different from the mechanism in Daphnia and carp. A probable reason is that V. fischeri is a unicellular organism with low lipid content, and hence both ionized and non-ionized forms of benzoic acids can easily cross the cell membrane and contribute to toxicity.

Validation of Vibrio fisheri acute toxicity data: mechanism of action-based QSARs for non-polar narcotics and polar narcotic phenols

Quantitative structure-activity relationships (QSARs) have been utilized to validate toxicity data to Vibrio fisheri (the acute Microtox test) at each of the 5-, 15- and 30-min endpoints. Statistically robust QSARs were found for chemicals acting by a non-polar narcosis mechanism of action (MOA). The baseline, non-polar narcosis QSARs were similar to those found in other aquatic organisms, thus indicating that for this MOA, the acute V. fisheri assay makes a suitable surrogate for higher test species. For the toxicity of phenols, previously modeled in other species with parameters describing membrane transport (i.e. hydrophobicity) and soft electrophilicity or ionization, no significant relationships were obtained. The use of acute V. fisheri toxicity data is not recommended for extrapolation to higher species for these compounds. Despite this recommendation, it was noted, however, that a 'minimal' phenolic toxicity was apparent that was analogous to polar narcosis in other species. This confirmed that a polar narcosis MOA does operate in the acute V. fisheri system analogous to other species. For all the compounds considered, there were highly significant correlations between the toxicities at each endpoint, indicating that data from different time endpoints were effectively interchangeable.