Multiple Computer Automated Structure Evaluation Research Articles

A structural analysis of the differential cytotoxicity of chemicals in the NCI-60 cancer cell lines

The primary functions of cancer chemotherapeutic agents are not only to inhibit the growth or kill the cancer cells, but to do so without eliciting unreasonable cytotoxic effects on the healthy cells and to withstand the ability of the cancer cells to develop resistance against it. This has unfortunately been proven so far to be a very difficult objective. In this perspective, the ability of small molecules (anti- tumor agents) to ‘see’ different cell types differently can be a key attribute. Thus the term ‘differential cytotoxicity’ is normally used to describe the drug’s specificity. In the present paper, we have quantified differential cytotoxicity from a study of the chemicals tested in the National Cancer Institute’s Developmental Therapeutics Program. The MULTICASE (Multiple Computer Automated Structure Evaluation) methodology was used to discover statistically significant structural fragments (biophores) related to the differential cytotoxicity of the compounds. We found that even small structural features often become important in this regard which is evident from the biophores that were found in structurally diverse chemicals. By utilizing the difference between the raw and normalized differential cytotoxicity indices, we found that the α,β-unsaturated carbonyl group (O C C CH 2) is the major biophore associated with compounds with essentially parallel concentration profiles in the cell lines in question. These compounds have high non-normalized differential cytotoxicity but considerably low normalized differential cytotoxocity. The models developed were cross validated for their predictive ability.

Structure–activity considerations and in vitro approaches to assess the genotoxicity of 19 methane-, benzene- and toluenesulfonic acid esters

Sulfonic acid esters are considered as potentially alkylating agents that may exert genotoxic effects in bacterial and mammalian cell systems. One possible source of human exposure stems from drug synthesis when the salt-forming agents methanesulfonic acid, benzenesulfonic acid or p-toluenesulfonic acid are used together with alcoholic solvents such as methanol, ethanol and propanol. In this study computer-assisted structural considerations and in vitro approaches (Ames mutagenicity test using Salmonella typhimurium strains TA98 and TA100, and the micronucleus test using L5178Y mouse lymphoma cells) were used to assess the genotoxic properties of 19 sulfonic esters. While all esters may be principally active as genotoxicants based on the presence of the sulfonate moiety, the statistical correlative multiple computer automated structure evaluation (MCASE) system (MC4PC version 1.0) using the Ames mutagenicity A2I module (version 1.54), rank-ordered the activity of the benzenesulfonic acid esters in the Ames test negligible due to an inactivating modulator and a deactivating fragment, whereas the methane- and toluenesulfonic acid esters were predicted to be positive in this test. In the Ames test, with the exception of the p-toluenesulfonic acid ethyl and iso-butyl esters, all compounds came out positive in Salmonella strain TA100. Methanesulfonic iso-propyl, sec-butyl and benzenesulfonic acid iso-propyl ester also showed mutagenic potential in strain TA98. In general, differences between results seen in Ames tests performed with or without metabolic activation were rather small. In L5178Y mouse lymphoma cells, benzenesulfonic acid n- and iso-butyl ester and p-toluenesulfonic acid iso-butyl ester did not increase the number of cells containing micronuclei. The other esters were positive in this micronucleus test; however, methanesulfonic acid iso-butyl ester was found to be only weakly positive at excessively cytotoxic concentrations. These compounds were generally found to be more potent with regard to micronucleus induction when tested without metabolic activation (20 h treatment). In conclusion, the iso-propyl esters of the three sulfonic acids under study were found to be the strongest mutagens, either when tested in the Ames test or the micronucleus assay, whereas p-toluenesulfonic acid iso-butyl ester was the only compound shown to be devoid of a genotoxic potential in both tests.

Searching for an Enhanced Predictive Tool for Mutagenicity

The Multiple Computer Automated Structure Evaluation (MCASE) program was used to evaluate the mutagenic potential of organic compounds. The experimental Ames test mutagenic activities for 2513 chemicals were collected from various literature sources. All chemicals have experimental results in one or more Salmonella tester strains. A general mutagenicity data set and fifteen individual Salmonella test strain data sets were compiled. Analysis of the learning sets by the MCASE program resulted in the derivation of good correlations between chemical structure and mutagenic activity. Significant improvement was obtained as more data was added to the learning databases when compared with the results of our previous reports. Several biophores were identified as being responsible for the mutagenic activity of the majority of active chemicals in each individual mutagenicity module. It was shown that the multiple-database mutagenicity model showed a clear advantage over normally used single-database models. The expertise produced by this analysis can be used to predict the mutagenic potential of new compounds.

Quantitative structure‐activity relationships for predicting mutagenicity and carcinogenicity

Quantitative structure-activity relationships (QSARs) for predicting mutagenicity and carcinogenicity were reviewed. The QSARs for predicting mutagenicity and carcinogenicity have been mostly limited to specific classes of chemicals (e.g., aromatic amines and heteroaromatic nitro chemicals). The motivation to develop QSARs for predicting mutagenicity and carcinogenicity to screen inventories of chemicals has produced four major commercially available computerized systems that are able to predict these endpoints: Deductive estimation of risk from existing knowledge (DEREK) toxicity prediction by komputer assisted technology (TOPKAT), computer automated structure evaluation (CASE), and multiple computer automated structure evaluation (Multicase). A brief overview of these and some other expert systems for predicting mutagenicity and carcinogenicity is provided. The other expert systems for predicting mutagenicity and carcinogenicity include automatic data analysis using pattern recognition techniques (ADAPT), QSAR Expert System (QSAR-ES), OncoLogic computer optimized molecular parametric analysis of chemical toxicity system (COMPACT), and common reactivity pattern (COREPA).

Multiple computer‐automated structure evaluation study of aquatic toxicity. III. Vibrio fischeri

An acute toxicity model was constructed on the basis of 901 chemicals tested for toxicity against the luminescent bacteria Vibrio fischeri (formerly Photobacterium phosphoreum, the Microtox test). The model was created using the Multiple Computer-Automated Structure Evaluation (M-CASE) program. The model can correctly predict acute toxicity for 92% of the compounds with an error averaging 0.55 log units per median effect concentration (EC50). The main toxicophores, corresponding to polar and nonpolar narcosis, and other types of reactive chemicals were identified.

MCASE study of the multidrug resistance reversal activity of propafenone analogs.

A database containing 130 propafenone type chemicals which have been tested for their multidrug resistance (MDR) reversal activity was compiled. Using the Multiple Computer-Automated Structure Evaluation (MCASE) program to analyze this database, an underlying relationship between MDR reversal activity and octanol/water partition coefficient was found. An MDR reversal model was created based on this database by the baseline activity identification algorithm (BAIA) of the MCASE program. The main phamacophores relevant to MDR reversal activity were identified.

Structure–specificity relationships for haloalkane dehalogenases

A structural analysis of the substrate specificity of hydrolytic dehalogenases originating from three different bacterial isolates has been performed using the multiple computer-automated structure evaluation methodology. This methodology identifies structural fragments in substrate molecules that either activate or deactivate biological processes. The analysis presented in this contribution is based on newly measured dehalogenation data combined with data from the literature (91 substrates). The enzymes under study represent different specificity classes of haloalkane dehalogenases (haloalkane dehalogenase from Xanthobacter autotrophicus GJ10, Rhodococcus erythropolis Y2, and Sphingomonas paucimobilis UT26). Three sets of structural rules have been identified to explain their substrate specificity and to predict activity for untested substrates. Predictions of activity and inactivity based on the structural rules from this analysis were provided for those compounds that were not yet tested experimentally. Predictions were also made for the compounds with available experimental data not used for the model construction (i.e., the external validation set). Correct predictions were obtained for 28 of 30 compounds in the validation set. Incorrect predictions were noted for two substrates outside the chemical domain of the set of compounds for which the structural rules were generated. A mechanistic interpretation of the structural rules generated provided a fundamental understanding of the structure-specificity relationships for the family of haloalkane dehalogenases.

Multiple computer‐automated structure evaluation study of aquatic toxicity II. Fathead minnow

AbstractAn acute toxicity model was constructed on the basis of experimental data for 685 chemicals tested for toxicity for fathead minnow. The multiple computer‐automated structure evaluation (M‐CASE) program was used for the construction of the model. Based on a comparison between our results and published results, we found that the methodology is able to describe acute toxicity for the fathead minnow with high accuracy. The model incorporates the concept of a baseline activity as one of the parameters for the correlation as well as others parameters such as the presence of biophores, hardness‐softness parameters, and other characteristics determined from quantum mechanical calculations. By using its artificial intelligence algorithm, M‐CASE chooses automatically the most suitable set of parameters for evaluating the minnow toxicity of any organic molecule. We found that M‐CASE can correctly predict acute toxicity for minnow for 80% of organic compounds with an average error of only 0.4 log units of lethal dose. The main toxicophores, corresponding to polar narcosis and to other types of reactive chemicals, were identified.

Multiple computer-automated structure evaluation model of the plasma protein binding affinity of diverse drugs

A drug protein binding model was constructed on the basis of protein-affinity data for154 drugs. The Multiple Computer-Automated Structure Evaluation program (M-CASE) was used for the construction of the model, which separates the total data set into groups of drugs with common structural features. For each of these groups, a multiparameter Quantitative Structure–Activity Relationship (QSAR) was obtained. The most general structural fragment for all investigated drugs is a part of the phenyl ring. The lipophilicity represented by the octanol–water partition coefficient was also found to be a significant parameter for each local QSAR. The model was shown to be able to predict correctly the percentage of drug bound in plasma for ∼80% of compounds with an average error of only ∼14%.

Structural determinants associated with risk of human developmental toxicity

OBJECTIVES: Identifying drugs or chemicals that represent hazards to human development is a continuous challenge. Of the approximately 60,000 chemicals in commercial use only 5% have been evaluated for developmental toxicity. Identification of inexpensive, rapid, validated techniques to demonstrate chemical hazards for the human embryo or fetus is the objective of this research. STUDY DESIGN: This research explored identification of structure activity predictors associated with human developmental toxicity by means of MULTICASE (multiple computer-automated structure evaluation), an algorithm that evaluates associations between chemicals and their constituent fragments and a biologic response. This algorithm allows identification of chemicals (and specific substructures) that may be human developmental toxicants. Developmental toxicity data were compiled from two sources (the Teratogen Information System and Food and Drug Administration guidelines) and analyzed to identify structural determinants (biophores) associated with human developmental toxicity. RESULTS: This analysis identified 17 biophores associated with human developmental toxicity. Testing the biophores against the learning set demonstrated 99% concordance, 100% sensitivity, and 98% specificity. Cross-validation studies were conducted, in which the original database was randomly separated into five learning and test sets; these demonstrated a mean concordance of 73%, with a mean sensitivity of 63% and a mean specificity of 79%. CONCLUSIONS: The MULTICASE structure-activity model is useful for identifying potential human developmental toxicants, as well as serving as a starting point for mechanistic investigations. (Am J Obstet Gynecol 1997;176:799-806.)

Prediction of the carcinogenicity of a second group of organic chemicals undergoing carcinogenicity testing.

Twenty-four organic compounds currently undergoing testing within cancer bioassays under the aegis of the U.S. National Toxicology Program (NTP) were submitted to the computer automated structure evaluation (CASE) and multiple computer automated structure evaluation (MULTICASE) system for predictions of activity. Individual predictions resulting from the NTP combined rodent, NTP mouse, Carcinogenic Potency Database (CPDB) combined rodent, and CPDB mouse databases were combined using Bayes' theorem to yield an overall probability of rodent carcinogenicity. Based upon an arbitrary probability cut-off of 0.50, nine compounds were predicted to be rodent carcinogens. The predicted carcinogens are chloroprene, 1-chloro-2-propanol, codeine, emodin, furfuryl alcohol, isobutyraldehyde, primaclone, sodium xylenesulfonate, and t-butylhydroquinone.

Computer-aided study of the relationship between structure and antituberculosis activity of a series of isoniazid derivatives

Abstract The Multiple Computer Automated Structure Evaluation (MultiCASE) program was used to analyze the relationship between the structure and antituberculous activity of a series of 136 hydrazides, most of them isoniazid related. The structural features revealed by this analysis are discussed. The most significant one seemed to be the distance between the pyridinic nitrogen and the terminal nitrogen of the hydrazido group. Given the affiliation of these two heteroatoms with a planar conjugated system, we suggest that Schiff base chemistry similar to that of vitamin B 6 may be involved in the mode of action of isoniazid and its related compounds. A mechanism of action of isoniazid is proposed and suggestions for the design of new isoniazid-type drugs are made.

Quantitative structure-agonist activity relationship of capsaicin analogues

The MULTIple Computer Automated Structure Evaluation (MULTICASE) methodology has been used to study the quantitative structure-agonist activity relationship of a series of capsaicin agonists. A number of substructures and physiochemical properties of capsaicin analogues were identified as being responsible for high agonist potency. The optimal log P value for the agonist potency as estimated from QSAR analysis is 5.12. It was also found that a cluster of inactive molecules in the database have lipophilicity values below 2.94. Molecular modeling was employed to elucidate the detailed structural features of the pharmacophore of capsaicin analogues. Systematic conformational analysis has shown that the activity of capsaicin analogues strongly depends upon their ability to reach the required conformational profile. Based upon these observations, a three-dimensional pharmacophore model for the capsaicin-receptor interactions is proposed.

Application of the multiple computer automated structure evaluation methodology to a quantitative structure–activity relationship study of acidity

AbstractWe applied the Multiple Computer Automated Structure Evaluation (Multi‐CASE) program to the analysis of the relationship between the structure of 2464 organic acids and their (first) pKa values. By using the self‐created expert dictionary of molecular attributes pertinent to acidity, the program could make successful a priori prediction of the acidity of new organic compounds. © 1994 by John Wiley & Sons, Inc.

In vitro anti-Mycobacterium avium activities of quinolones: predicted active structures and mechanistic considerations

The relationship between the structures of quinolones and their anti-Mycobacterium avium activities has been previously derived by using the Multiple Computer-Automated Structure Evaluation program. A number of substructural constraints required to overcome the resistance of most of the strains have been identified. Nineteen new quinolones which qualify under these substructural requirements were identified by the program and subsequently tested. The results show that the substructural attributes identified by the program produced a successful a priori prediction of the anti-M. avium activities of the new quinolones. All 19 quinolones were found to be active, and 4 of them are as active or better than ciprofloxacin. With these new quinolones, the updated multiple computer-automated structure evaluation program structure-activity relationship analysis has helped to uncover additional information about the nature of the substituents at the C5 and C7 positions needed for optimal inhibitory activity. A possible explanation of drug resistance based on the observation of suicide inactivation of bacterial cytochrome P-450 by the cyclopropylamine moiety has also been proposed and is discussed in this report. Furthermore, we confirm the view that the amount of the uncharged form present in a neutral pH solution plays a crucial role in the drug's penetration ability.

Multiple Computer Automated Structure Evaluation Methodology as an Alternative toIn VivoEye Irritation Testing

The Multiple Computer Automated Structure Evaluation (Multi-CASE) methodology was applied to the semi-quantitative analysis of eye irritation potential of 186 organic chemicals. The resulting expert dictionary of substructural attributes indicates that the following functionalities are linked to eye irritation potential: carboxylate and sulphonate groups in anionic surfactants, phenolic moieties in non-ionic surfactants, amino groups, halogenated and unsaturated structures, anhydrides and epoxides. Ester groups were recognised as having a low irritation potential. The Multi-CASE program successfully predicted a priori the eye irritation potential of: 1) 21 individual chemicals; 2) chemical mixtures such as coconut oil, hydrogenated castor oil, rice bran, eucalyptus oils and potassium alginate; and 3) polymers,’ including polyvinyl alcohols, polyvinyl butyral, polyvinyl pyrrolidone, corn starch and nylon. It was found that the structure of the repeat units together, and not that of the individual monomers, is the best predictor of the eye irritation potential of the corresponding polymers. It appears that the repeat units of polymers dilute out the irritation potential of terminal groups normally expected to contribute significant irritation potential to chemicals of lower molecular weight.The Multi-CASE eye irritation model can be used for: 1) in vitro prescreening for eye irritation potential; and 2) design and modification of chemicals when low eye irritation potential is important.