Mixture toxicity was determined for 30A+B combinations. Chemical A was the reactive soft electrophile methyl-2-chloroacetoacetate (M2CA), and chemical B was one of 30 reactive or non-reactive agents. Bioluminescence inhibition in Allovibrio fischeri was measured after 15-, 30-, and 45-minutes of exposure for A, B, and the mixture (MX) with ECx (i.e., EC25, EC50, and EC75) values being calculated. Concentration-response curves (CRCs) were developed for A and B at each exposure duration and used to create predicted CRCs for the concentration addition (CA) and independent action (IA) mixture toxicity models. Likewise, MX CRCs were generated and compared with model predictions, along with the calculation of additivity quotient (AQ) and independence quotient (IQ) values. Mixture toxicity vs. the models showed various combined effects, including toxicity that was slightly greater than IA and/or CA, consistency with CA, IA or both models, effects that were less toxic than expected for either model and antagonism. Simple linear regression analyses of time-dependent toxicity (TDT) data showed very strong correlations (r2 ≥ 0.997) for B-TDT vs. the average TDT for A and B. Likewise, for both CA and IA, multiple linear regression analyses showed strong correlations (r2 > 0.960) between MX ECx and either CA ECx and AQx or IA ECx and IQx values at each exposure duration. The results show that analyses of binary mixture toxicity data produced linear relationships resulting in equations that can effectively predict such toxicity.

The value of time-dependent toxicity (TDT) data in predicting mixture toxicity was examined. Single chemical (A and B) and mixture (A+B) toxicity tests using Microtox(®) were conducted with inhibition of bioluminescence (Vibrio fischeri) being quantified after 15, 30 and 45-min of exposure. Single chemical and mixture tests for 25 sham (A1:A2) and 125 true (A:B) combinations had a minimum of seven duplicated concentrations with a duplicated control treatment for each test. Concentration/response (x/y) data were fitted to sigmoid curves using the five-parameter logistic minus one parameter (5PL-1P) function, from which slope, EC25, EC50, EC75, asymmetry, maximum effect, and r(2) values were obtained for each chemical and mixture at each exposure duration. Toxicity data were used to calculate percentage-based TDT values for each individual chemical and mixture of each combination. Predicted TDT values for each mixture were calculated by averaging the TDT values of the individual components and regressed against the observed TDT values obtained in testing, resulting in strong correlations for both sham (r(2)=0.989, n=25) and true mixtures (r(2)=0.944, n=125). Additionally, regression analyses confirmed that observed mixture TDT values calculated for the 50% effect level were somewhat better correlated with predicted mixture TDT values than at the 25 and 75% effect levels. Single chemical and mixture TDT values were classified into five levels in order to discern trends. The results suggested that the ability to predict mixture TDT by averaging the TDT of the single agents was modestly reduced when one agent of the combination had a positive TDT value and the other had a minimal or negative TDT value.

In mixture toxicity, concentration-effect data are often used to generate conclusions on combined effect. While models of combined effect are available for such assessments, proper fitting of the data is critical to obtaining accurate conclusions. In this study an asymmetry parameter (s) was evaluated for data-fitting and compared with our previous approach. Inhibition of bioluminescence was assessed with Vibrio fischeri at 15, 30 and 45-min of exposure with seven or eight concentrations and a control (each duplicated) for each single-chemical (A or B) and mixture (A:B). Concentration-effect data were fitted to sigmoid curves using the four-parameter logistic function (4PL) and the five-parameter logistic minus one-parameter (5PL-1P) function. For the 4PL, parameters included minimum effect, maximum effect, EC(50) and slope, while for the 5PL-1P the minimum effect parameter was removed and an asymmetry parameter was added. A total of 72 mixture toxicity data sets were evaluated, representing 432 single-chemical and 216 mixture curves. Mean coefficients of determination (r(2)) for all 648 curves showed that the 5PL-1P gave better fitting (0.9982 ± 0.0018) than the 4PL (0.9973 ± 0.0030). For both functions, the sum-of-squares of the residuals (SS-Res) was determined for each curve. The 5-parameter rational regression best described the relationship between the decrease in sum-of-squares of the residuals (i.e., 4PL: SS-Res - 5PL-1P: SS-Res) and log s, with fitting improved the most at low values of s (s<0.8). This held even when curves with r(2) values ≤ 0.9970 were removed from the analyses. Subsequent review of the combined effects obtained via the 4PL and the 5PL-1P functions resulted in a change in the interpretation of combined effect in 39/216 (18%) cases.

A diverse set of 60 haloaliphatic compounds were evaluated for reactivity with cysteine thiol groups in the previously described RC(50) assay using glutathione (GSH) as a model nucleophile. Reactivity was quantified by the RC(50) value, the concentration of test compound that produced 50% reaction of the GSH thiol groups in 120 min. Under standard conditions, RC(50) values are mathematically proportional to reciprocal rate constants. Quantitative structure-activity relationship (QSAR) analysis correlating acute aquatic toxicity (IGC(50)) to Tetrahymena pyriformis with RC(50) values was carried out. It was found that subdivision of the compounds into subdomains according to their reaction mechanism characteristics enabled toxicity-reactivity relationships to be identified. The largest subdomain consisting of 22 compounds in which a primary halogen is alpha to a carbonyl or other electronegative unsaturated group and which can be confidently assigned as S(N)2 electrophiles fits the equation pIGC(50) (mM) = 0.94 (+/-0.07) pRC(50) (mM) + 1.34 (+/-0.07), n = 22, r(2) = 0.889, r(2)(adj) = 0.884, s = 0.27, and F = 161. Compounds in which the halogen is not alpha to an unsaturated group are not reactive in the GSH assay and do not exhibit reactive toxicity to T. pyriformis. Compounds tested in which the halogen is alpha to an unsaturated nonelectronegative group were found to be less toxic in the assay than predicted by the above QSAR equation. Within a subdomain of 21 compounds having a halogen alpha to an electronegative unsaturated group that, in the absence of experimental evidence, could not be confidently assigned as S(N)2 electrophiles, 2-bromoalkanoates of general structure R(1)CHBrCO(2)R(2), 2-bromopropionamide, and 2-haloalkanoic acids of general formula R(1)CHXCO(2)H (nine compounds in total) are all well-predicted by the above equation. Of the other 12 compounds of this subdomain, eight are substantially less toxic than predicted by the above equation and are considered to react differently, whereas the alpha-halonitriles (four compounds) are more toxic than predicted and fit a correlation of their own: pIGC(50) = 1.01 (+/-0.05) pRC(50) + 2.04 (+/-0.05), n = 4, r(2) = 0.995, r(2)(adj) = 0.992, s = 0.08, and F = 381, with a similar slope but larger intercept. An explanation in terms of their physical chemistry and possible involvement of released cyanide ion is suggested.

ABSTRACT A Saccharomyces cerevisiae strain, capable of autonomous bioluminescence, was engineered to respond to androgenic chemicals. The strain, S. cerevisiae BLYAS, contains the human androgen receptor in the chromosome and was constructed by inserting a series of androgen response elements between divergent yeast promoters GPD and ADH1 on pUTK401 that constitutively expressed luxA and luxB to create pUTK420. Cotransformation of this plasmid with a second plasmid (pUTK404), containing the genes required for aldehyde synthesis ( luxCDE ) and FMN reduction ( frp ), yielded a bioluminescent bioreporter responsive to androgenic chemicals. Using dihydrotestosterone (DHT) as a standard, the response time and the 50% effective concentration values were 3 to 4 h and (9.7 ± 4.6) × 10 −9 M, respectively. The lower limit of detection in response to DHT was 2.5 × 10 −9 M, and in response to testosterone it was 2.5 × 10 −10 M. This strain is suitable for high-throughput screening of chemicals with potential for remote environmental monitoring systems because of the assay speed, sensitivity, and self-containment.

An approach for predicting acute aquatic toxicity, in the form of a quantitative structure–activity–activity relationship (QSAAR), is described. This study assessed relative toxic effects to a fish, Pimephales promelas, and a ciliate, Tetrahymena pyriformis, and attempted to form relationships between them. A good agreement between toxic potencies (R2 = 0.754) was found for a chemically diverse dataset of 364 compounds, when using toxicity to the ciliate as a surrogate to that for fish. This relationship was extended by adding three theoretical structural descriptors of the molecules. The inclusion of these descriptors improved the relationship further (R2 = 0.824). The structural features that were found to improve the extrapolation between the toxicity to the two different species were related to the electron distribution of the carbon skeleton of the toxicant, its hydrogen-bonding ability, and its relative nitrogen content. Such a QSAAR approach provides a potential tool for predicting the toxicities of chemicals for environmental risk assessment and thus for reducing animal tests.

Valproate has been used for over 30 years as a first-line treatment for epilepsy. In recent years, prenatal exposure to valproate has been associated with teratogenic effects, limiting its use in women that are pregnant or of childbearing age. However, despite its potential detrimental effects on development, valproate continues to be prescribed at high rates in pediatric populations in some countries. Animal models allow us to test hypotheses regarding the potential effects of postnatal valproate exposure on neurobehavioral development, as well as identify potential mechanisms mediating observed effects. Here, we tested the effect of early postnatal (P4–P11) valproate exposure (100 mg/kg and 200 mg/kg) on motor and affective development in two strains of mice, SVE129 and C57Bl/6N. We also assessed the effect of early valproate exposure on regional BDNF protein levels, a potential target of valproate, and mediator of neurodevelopmental outcomes. We found that early life valproate exposure led to significant motor impairments in both SVE129 and C57Bl/6N mice. Both lines of mice showed significant delays in weight gain, as well as impairments in the righting reflex (P7–8), wire hang (P17), open field (P12 and P21), and rotarod (P25 and P45) tasks. Interestingly, some of the early locomotor effects were strain- and dose-dependent. We observed no effects of valproate on early markers of anxiety-like behavior. Importantly, early life valproate exposure had significant effects on regional BDNF expression, leading to a near 50% decrease in BDNF levels in the cerebellum of both strains of mice, while not impacting hippocampal BDNF protein levels. These observations indicate that postnatal exposure to valproate may have significant, and region-specific effects, on neural and behavioral development, with specific consequences for cerebellar development and motor function.

A diverse series of polarized alpha,beta-unsaturated and related compounds were evaluated for reactivity with a spectrophotometric assay using the sulfhydryl group in the form of the cysteine residue of the tripeptide GSH as a model nucleophile. The reactive end point (RC 50) calculations were compared to previously described structural alerts based on conventional organic chemistry. This comparison focused on polarized alpha,beta-unsaturates, including ones containing an aldehyde, ketone, ester, sulfoxide, sulfone, sulfonate, nitro, or cyano moiety as well as ortho- and para-pyridino compounds and ortho- and para-quinones. The alerts were coded by substructure and are available in open-source software ( http://sourceforge.net/projects/chemeval). Comparisons of reactivity between selected analogues revealed that only the polarized alpha,beta-unsaturates were reactive. These results verified the coded structural alerts that define the applicability domain for Michael acceptor electrophiles.

A diverse series of aliphatic alpha,beta-unsaturated esters, ketones, and aldehydes were evaluated for reactivity with the model nucleophile sulfhydryl group in the form of the cysteine residue of the tripeptide glutathione; the reactive end point (RC50) was then related to aquatic toxicity (IGC(50)) assessed in the Tetrahymena pyriformis population growth impairment assay. The substructure specific to all tested reactive substances, an olefin conjugated to a carbonyl group, is inherently electrophilic and conveys the potential to act by way of Michael-type nucleophilic addition. All such unsaturated compounds are inherently acutely toxic. However, their toxicity is difficult to model with conventional descriptors since toxicity is independent of both hydrophobicity and molecular orbital electrophilicity but dependent on the specific molecular structure. While methacrylates typically did not attain an RC50 value at saturation, a linear relationship [log (IGC(50)(-1)) = 0.936[log (RC50-1)] + 0.508, where n = 41, r(2) = 0.846, q(2) = 0.832, s = 0.35, F = 214, and Pr > F = 0.0001] was observed between aquatic toxicity and reactivity for the other carbonyl-containing alpha,beta-unsaturated chemicals.

Aquatic toxicity data in the TETRATOX assay and reactivity data in an abiotic thiol assay were collected for a series of aliphatic isothiocyanates. These compounds can act as Michael-type acceptors with N-hydro-C-mercapto-addition to cellular thiols as a molecular mechanism of action. Comparison of both toxicity and reactivity among the analogues revealed that derivatives with a branch hydrocarbon moiety, especially branched in the β-position were less toxic and less reactive. In contrast, the di-isothiocyanate and the allyl and propargyl derivatives are more toxic than their 1-alkyl homologues. The toxicity and reactivity differences are consistent but except for the tert-butyl-derivative not remarkable. The differences are due to variations in steric hindrance at the reaction center. For the mono-isothiocyanates compounds toxicity (IGC(50)) is linearly related to thiol reactivity (EC(50)): log(1/IGC(50))=1.33(log(1/EC(50)))-0.41; n=23, s=0.24, r(2)=0.911, q(2)=0.907, F=215.

The development of non-animal methods to predict the potential of chemicals to cause skin sensitization is of great importance. On the basis of many published studies into the underlying chemical mechanisms skin sensitization, the immunological priming which leads to the disease allergic contact dermatitis, is recognized as a reactive chemistry endpoint. Consequently, the combination of chemical assays with in vitro techniques may provide a useful surrogate to animal testing for skin sensitization. This study attempts to investigate the relationship between skin sensitization assessed in the local lymph node assay (LLNA) initially and a thiol reactivity index based on glutathione (GSH), pEC50 thiol (EC50 being defined as the concentration of the test substance which gives 50% depletion of free thiol under standard conditions) in combination with a measure of cytotoxicity (pIGC50) to Tetrahymena pyriformis (TETRATOX). The pEC50 thiol values and the pIGC50 values were determined for twenty-four compounds for which LLNA test data were available. Thiol reactivity was found to discriminate sensitizers from non-sensitizers according to the rule: pEC50 thiol>−0.55 indicates that the compound will be a skin sensitizer. However, because of metabolic activation a pEC50 thiol<−0.55 does not necessarily mean that the compound will be a non-sensitizer. Excess toxicity to T. pyriformis (i.e. the extent of toxic potency over that expected by non-polar narcosis) was determined in order to assess biological reactivity. The best discrimination based on excess toxicity in the TETRATOX assay was given by the “rule”: excess toxicity>0.50 indicates that the compound will be a skin sensitizer. These approaches become more powerful when combined. When taken together, the thiol and TETRATOX assays predict the sensitization potential of 23 of the 24 compounds correctly. α-Hexylcinnamic aldehyde is incorrectly predicted to be a non-sensitizer, whereas LLNA results suggest it may be a weak sensitizer, this inaccuracy being rationalized in terms of its high hydrophobicity. Due to the selectivity of electro(nucleo)philic reactions some sensitizing compounds will not be identified using a single nucleophile such as thiol.

The toxicity of 30 binary combinations of 10 soft electrophiles was examined in Microtox using dose–response curve (DRC) analysis. Chemicals from three groups of soft electrophiles—vinyl Michael acceptors (I—react with a thiol group), dicarbonyl reactive agents (II—react with a primary amine), and α-haloactivation compounds (III—react with a thiol group)—were selected for testing to evaluate the relationship between molecular site of chemical action and combined toxic effect. For each combination tested, each single agent was tested alone at six duplicated concentrations and three 1:1 mixtures of the agents were also tested, each at six duplicated concentrations. Exposure duration was 15min for each single agent and mixture test. Sigmoid DRCs for each single chemical and mixture were constructed and the single chemical curves were used to develop a theoretical dose-addition DRC for the combination. Additivity quotient (AQ) values for slope and EC50 were calculated by dividing the actual mixture slope or EC50 for a given combination by the predicted slope or EC50, respectively, from the theoretical dose-addition DRC. Three criteria were selected for value in determining the combined effect obtained for each combination: (1) slope AQ 95% confidence interval (CI) overlap with 1.0 (1.0=dose addition), (2) EC50 AQ 95% CI overlap with 1.0, and (3) mean mixture data point 95% and 99% CI overlap with the theoretical dose-addition DRC. Each of three sham combinations showed combined effects consistent with dose addition for each criterion. Dose addition was expected for 15 nonsham combinations (nine within-group combinations and six group I:III combinations) and a nondose-additive effect was expected for 12 combinations (all I:II and II:III combinations). Actual combined effects obtained by incorporating all three criteria (noted above) showed only six instances of dose addition. Therefore, time-dependent toxicity (TDT) tests of each soft electrophile alone and for three nonpolar narcotic chemicals alone were conducted, using 15-, 30-, and 45-min exposure durations, to assess the time-dependent nature of the toxicity. Results of the TDT tests suggested that five had fully (or nearly fully) TDT (interpreted as an irreversible effect representing one molecular site of action), five of the soft electrophiles had partially TDT (i.e., representing two or more molecular sites of action for the agents, one irreversible and one reversible), and the three nonpolar narcotics had no TDT (i.e., a fully reversible toxic effect). With this TDT information, the combined effects for 25 of the 27 mixtures, although rather complex, could be explained. It is noteworthy that all combined effects obtained, whether concluded to be dose-additive or not, were close to dose-additive for hazard assessment purposes.

Toxicity data for 82 aliphatic chemicals with an alpha,beta-unsaturated substructure were compiled. Toxicity was assessed in the 2-day Tetrahymena pyriformis population growth impairment assay. Toxic potency [log(IGC50(-1))] for most of these chemicals was in excess of baseline narcosis as quantified by the 1-octanol/water partition coefficient (log K(ow)). The toxicity of the alpha,beta-unsaturated aldehydes was modeled well by log K(ow) in conjunction with the sum of partial charges on the vinylene carbon atoms (Q(C4) + Q(C3)) and the energy of the lowest unoccupied molecular orbital (E(lumo)). These electronic descriptors were also successful at modeling the toxicity of alpha,beta-unsaturated ketones. The toxicity of a range of acrylates was constant within about 0.2 of a log unit. Conversely, the toxicity of methacrylates and esters containing the vinylene group varied considerably and was explained by their hydrophobicity. The comparison of the quantitative structure-activity relationship (QSAR) for the methacrylates and esters with that for non-polar narcosis showed little significant difference and hence suggested that substitution on the carbon-carbon double bond in the methacrylates and vinylene unsaturated esters does not enhance toxicity over that of baseline. Substitution on the carbon-carbon double bond in the alpha,beta-unsaturated aldehydes resulted in toxicity that was similar to that for saturated derivatives. Although an excellent hydrophobicity-dependent QSAR was developed for the esters containing ethynylene group, these compounds are considered to act as Michael-type acceptors. Attempts to combine different groups of Michael-type acceptors into a single QSAR, based on mechanistically derived descriptors, were unsuccessful. Thus, the modeling of the toxicity of the alpha,beta-unsaturated carbonyl domain is currently limited to models for narrow subdomains.

The present study proposes a generic interspecies quantitative structure-activity relationship (QSAR) model that can be used to predict the acute toxicity of aldehydes to most species of aquatic organisms. The model is based on the flow-through fathead minnow (Pimephales promelas) 50% lethal concentration (LC50) data combined with other selected fish acute toxicity data and on the static ciliate (Tetrahymena pyriformis) 50% inhibitory growth concentration (IGC50) data. The toxicity of Schiff-base acting aldehydes was defined using hydrophobicity, as the calculated log 1-octanol/water partition coefficient (log Kow), and reactivity, as the donor delocalizability for the aldehyde O-site (D(O-atom)). The fish model [log 1/LC50 = -2.503(+/-1.950) + 0.480(+/-0.052) log Kow + 18.983(+/-6.573) D(O-atom), n = 62, r2 = 0.619, s2 = 0.241, F = 48.0, Q2 = 0.587] compares favorably with the ciliate model [log 1/IGC50 = -0.985(+/-1.309) + 0.530(+/-0.044) log Kow + 11.369(+/-4.350) D(O-atom), n = 81, r2 = 0.651, s2 = 0.147, F = 72.9, Q2 = 0.626]. The fish and ciliate surfaces appear to be parallel, because they deviate significantly only by their intercepts. These observations lead to the development of a global QSAR for aldehyde aquatic toxicity [log E(-1) = bE(Organism) + 0.505(+/-0.033) log Kow + 14.315(+/-3.731) D(O-atom), n = 143, r2 = 0.698, s2 = 0.187, S2(Fish) = 0.244, S2(Ciliate) = 0.149, F = 98, Q2 = 0.681]. The general character of the model was validated using acute toxicity data for other aquatic species. The aldehydes global interspecies QSAR model could be used to predict the acute aquatic toxicity of untested aldehydes and to extrapolate the toxicity of aldehydes to other aquatic species.

This work explores the possibilities of generalizing a variety of narcotic global quantitative structure–activity relationships used in aquatic toxicological studies. The models developed herein are based on the static ciliate (Tetrahymena pyriformis) population growth impairment (IGC50−1) data. Results are compared with models previously developed with the flow-through fish (Pimephales promelas) mortality (LC50−1) data. The modeling approach is an extension of a recently developed expert system coupled with the response-surface concept. The expert system characterizes the molecular domain of non-specifically acting chemicals, both for neutral and amine narcotics. Toxicity surface(s) are defined using hydrophobicity quantified by the log1-octanol/water partition coefficient (logKow) and orbital electrophilicity quantified by the energy of the lowest unoccupied molecular orbital (ELUMO) as global molecular descriptors. The ciliate response surface models: log(IGC50−1)=1.50+0.68logKow−0.13ELUMO; n=411, R2=0.890 and log(IGC50−1)=1.80+0.68logKow; n=51, R2=0.854 for neutral and amine narcotics, respectively, compares favorably with the fish model. The fish and ciliates response-surfaces appear to be parallel as they significantly deviate only by their intercepts.

Despite their importance as a research model, particularly in developmental toxicology investigations, there are few established standards for maintaining Xenopus spp. frogs in the laboratory. The authors review the literature on handling, housing, nutrition, and breeding of Xenopus spp. for optimal oocyte production.

The use of quantitative structure‐activity relationships (QSARs) in assessing potential toxic effects of organic chemicals on aquatic organisms continues to evolve as computational efficiency and toxicological understanding advance. With the ever‐increasing production of new chemicals, and the need to optimize resources to assess thousands of existing chemicals in commerce, regulatory agencies have turned to QSARs as essential tools to help prioritize tiered risk assessments when empirical data are not available to evaluate toxicological effects. Progress in designing scientifically credible QSARs is intimately associated with the development of empirically derived databases of well‐defined and quantified toxicity endpoints, which are based on a strategic evaluation of diverse sets of chemical structures, modes of toxic action, and species. This review provides a brief overview of four databases created for the purpose of developing QSARs for estimating toxicity of chemicals to aquatic organisms. The evolution of QSARs based initially on general chemical classification schemes, to models founded on modes of toxic action that range from nonspecific partitioning into hydrophobic cellular membranes to receptor‐mediated mechanisms is summarized. Finally, an overview of expert systems that integrate chemical‐specific mode of action classification and associated QSAR selection for estimating potential toxicological effects of organic chemicals is presented.

Numerous quantitative structure-activity relationships (QSARs) have been developed to predict properties, fate, and effects of mostly discrete organic chemicals. As the demand for different types of regulatory testing increases and the cost of experimental testing escalates, there is a need to evaluate the use of QSARs and provide some guidance to avoid their misuse, especially as QSARs are being considered for regulatory purposes. This paper provides some guidelines that will promote the proper development and use of QSARs. While this paper uses examples of QSARs to predict toxicity, the proposed guidelines are applicable to QSARs used to predict physical or chemical properties, environmental fate, ecological effects and health effects.

ChemInformVolume 34, Issue 39 Other Subjects Molecular Quantum Similarity Analysis of Estrogenic Activity. Ana Gallegos Saliner, Ana Gallegos Saliner Inst. Comput. Chem., Univ. Girona, E-17071 Girona, SpainSearch for more papers by this authorLluis Amat, Lluis Amat Inst. Comput. Chem., Univ. Girona, E-17071 Girona, SpainSearch for more papers by this authorRamon Carbo-Dorca, Ramon Carbo-Dorca Inst. Comput. Chem., Univ. Girona, E-17071 Girona, SpainSearch for more papers by this authorT. Wayne Schultz, T. Wayne Schultz Inst. Comput. Chem., Univ. Girona, E-17071 Girona, SpainSearch for more papers by this authorMark T. D. Cronin, Mark T. D. Cronin Inst. Comput. Chem., Univ. Girona, E-17071 Girona, SpainSearch for more papers by this author Ana Gallegos Saliner, Ana Gallegos Saliner Inst. Comput. Chem., Univ. Girona, E-17071 Girona, SpainSearch for more papers by this authorLluis Amat, Lluis Amat Inst. Comput. Chem., Univ. Girona, E-17071 Girona, SpainSearch for more papers by this authorRamon Carbo-Dorca, Ramon Carbo-Dorca Inst. Comput. Chem., Univ. Girona, E-17071 Girona, SpainSearch for more papers by this authorT. Wayne Schultz, T. Wayne Schultz Inst. Comput. Chem., Univ. Girona, E-17071 Girona, SpainSearch for more papers by this authorMark T. D. Cronin, Mark T. D. Cronin Inst. Comput. Chem., Univ. Girona, E-17071 Girona, SpainSearch for more papers by this author First published: 12 September 2003 https://doi.org/10.1002/chin.200339218AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume34, Issue39September 30, 2003 RelatedInformation