PubMed Health⌕ Search

Biomedical subjects

V K Gombar

Publications and source records attributed to V K Gombar.

11 recordsLinked to original sources
PubMed1999

Reliable assessment of logP of compounds of pharmaceutical relevance.

In an earlier study Quant. Struct.-Act. Relat. 1996, 15, 403-409 comparing the performance of 14 logP predictors it was concluded that predictions of logP values were significantly better for simple organic molecules than for drugs. Since the publication of this benchmark study, a logP predictor, VLOGP, has been developed in our group. In the work presented here, VLOGP is used to assess the logP values of the same 48 drugs as included in the benchmark comparison. VLOGP returned 79.2% "acceptable", 18.6% "disputable", and only 2.2% "unacceptable" logP values. the "acceptable", "disputable", and "unacceptable" logP values from the 14 other predictors, respectively, ranged between 27.1% and 72.9%, 16.7% and 41.7%, and 2.2% and 37.5%. Further, VLOGP resulted in a much tighter fit (mean squared deviation, m.s.d., = 0.197) between experimental and calculated values of logP compared with the other 14 methods for which the m.s.d. ranged between 0.247 and 1.068. The major differentiation between VLOGP and other predictors is that its application domain, called Optimum Prediction Space (OPS), is quantitatively defined, i.e., the structures to which VLOGP model should not be applied for predicting logP can be identified. This process is automated by implementation of VLOGP in the TOPKAT package.

Adrenergic beta-Antagonists↗
PubMed1995

Assessment of developmental toxicity potential of chemicals by quantitative structure-toxicity relationship models.

Statistically significant quantitative structure-toxicity relationship (QSTR) models have been developed for assessing developmental toxicity potential (DTP) of chemicals. Three submodels, one each for aliphatic, heteroaromatic and carboaromatic compounds, have been cross-validated to ascertain their robustness. The specificities of the models range from 86% to 97%, and their sensitivities between 86% and 89%. For convenient computer-assisted application, the models are installed in a toxicity assessment software package, TOPKAT, which has been recently enhanced with algorithms to identify whether or not a query structure is inside the optimum prediction space (OPS) of a QSTR model. Different functionalities of the TOPKAT program have been explained by assessing the DTP of a number of compounds not used in the model training sets. The DTP of 18 existing drugs was assessed using these models; the DT assay results were available for 5 of these. Three of these 5 molecules were identified to be inside the OPS and their TOPKAT assessment matched their experimental assignment.

Algorithms↗
PubMed1995

Assessment of effect levels of chemicals from quantitative structure-activity relationship (QSAR) models. I. Chronic lowest-observed-adverse-effect level (LOAEL).

With the multitude of new chemicals being synthesized and the paucity of long-term test data on chemicals that could be introduced into the environment, innovative approaches must be developed to determine the health and environmental effects of chemicals. Research was conducted to employ quantitative structure-activity relationship (QSAR) techniques to study the feasibility of developing models to estimate the noncarcinogenic toxicity of chemicals that are not addressed in the literature by relevant studies. A database of lowest-observed-adverse effect level (LOAEL) was assembled by extracting toxicity information from 104 U.S. EPA documents, 124 National Cancer Institute/National Toxicology Program (NCI/NTP) reports, and 6 current reports from the literature. A regression model, based on 234 chemicals of diverse structures and chemical classes including both alicyclic and aromatic compounds, was developed to assess the chronic oral LOAELs in rats. The model was incorporated into an automated computer package. Initial testing of this model indicates it has application to a wide range of chemicals. For about 55% of the compounds in the data set, the estimated LOAELs are within a factor of 2 of the observed LOAELs. For over 93%, they are within a factor of 5. Because of the paucity or absence of long-term toxicity data, the public health and risk assessment community could utilize such QSAR models to determine initial estimates of toxicity for the ever-increasing numbers of chemicals that lack complete pertinent data. However, this and other such models should be used only by expert toxicologists who must objectively look at the estimates thus generated in light of the overall weight of evidence of the available toxicologic information of the subject chemical(s).

Algorithms↗
PubMed1993

Carcinogenicity of azathioprine: an S-AR investigation.

Besides its use in the treatment of a variety of presumed autoimmune diseases, azathioprine is given as an immunosuppressant to patients who have had renal transplants. Though epidemiological studies have provided "sufficient" evidence of its carcinogenicity in humans, the carcinogenicity tests in rats and mice are considered to be inconclusive because of limitations in the design and results of these tests (IARC, 1981, 1987). Rosenkranz and Klopman (1991) used the CASE program to identify the structural features responsible for its carcinogenicity. They concluded that this genotoxic chemical was a carcinogen due to the presence of the molecular fragment C"-S-C=. The finding was based on the presence of this biophore fragment in five other compounds, namely: 2-amino-5-nitrothiazole, 2-mercaptobenzothiazole, fenthione, 4,4'-thiodianiline and nithiazide. Recently, Ashby (1992) has expressed concern over the validity of their findings. With the aim of contributing to this debate on the mechanism of carcinogenicity of azathioprine, we have analyzed the structural basis of carcinogenicity of azathioprine and the five support compounds using the carcinogenicity predictor of our toxicity prediction program, TOPKAT. The results, more in line with Ashby's concerns, indicate that no molecular fragment involving the S atom is associated with the carcinogenic properties of these molecules. According to the TOPKAT program the carcinogenicity, if any, of azathioprine is due to the NO2 electrophile because its other major structural features are found to be either associated with non-carcinogenicity or do not discriminate carcinogens from non-carcinogens.

Algorithms↗
PubMed1991

Estimation of maximum tolerated dose for long-term bioassays from acute lethal dose and structure by QSAR.

A quantitative structure-activity relationship (QSAR) model has been developed to estimate maximum tolerated doses (MTD) from structural features of chemicals and the corresponding oral acute lethal doses (LD50) as determined in male rats. The model is based on a set of 269 diverse chemicals which have been tested under the National Cancer Institute/National Toxicology Program (NCI/NTP) protocols. The rat oral LD50 value was the strongest predictor. Additionally, 22 structural descriptors comprising nine substructural MOLSTAC(c) keys, three molecular connectivity indices, and sigma charges on 10 molecular fragments were identified as endpoint predictors. The model explains 76% of the variance and is significant (F = 35.7) at p less than 0.0001 with a standard error of the estimate of 0.40 in the log (1/mol) units used in Hansch-type equations. Cross-validation showed that the difference between the average deleted residual square (0.179) and the model residual square (0.160) was not significant (t = 0.98).

Animals↗
PubMed1990

Salmonella mutagenicity and rodent carcinogenicity: quantitative structure-activity relationships.

Based on a compilation of 222 reports of rodent nominal lifetime carcinogenicity bioassays by the NCI/NTP on the one hand, and corresponding Salmonella mutagenicity bioassays (Ames tests) on the other, Ashby and Tennant (1988) have divided the carcinogens and non-carcinogens into genotoxic (Ames test positive) and non-genotoxic (Ames test negative) groups and discussed structural characteristics common to each of these groups. The Ames test alone was deemed to be adequate for the identification of genotoxicity because other short-term bioassays, and even combinations, or batteries, appeared to offer no significant advantages. From the results of this study it is possible to achieve (1) a division of the carcinogens into the same genotoxic and non-genotoxic groups, and (2) a division of the non-genotoxic compounds into the same carcinogenic and non-carcinogenic groups, solely on the basis of structure-activity relationships, with a classification accuracy of approx. 95%. (1) An equation comprising 8 sigma molecular charge descriptors, 2 molecular connectivity indices (MCIs), 2 kappa molecular shape descriptors and one MOLSTAC substructure descriptor achieved discrimination between genotoxic and non-genotoxic carcinogens with an accuracy of 94.5%. (2) Another equation comprising 8 sigma molecular charge descriptors, 3 MCIs, one kappa shape descriptor and 12 substructural descriptors achieved discrimination between non-genotoxic carcinogens and non-genotoxic non-carcinogens with an accuracy of 95.2%. These SAR models are suitable for the distinction between (1) genotoxic and non-genotoxic carcinogens and (2) carcinogenic and non-carcinogenic non-genotoxins, both in the absence of animal bioassay data.

Carcinogenicity Tests↗
PubMed1985

Structure-activity relationship studies of antiviral N-quinolin-4-yl-N'-benzylidenehydrazine derivatives by discriminant analysis.

The classification technique of linear discriminant analysis (LDA) is applied for studying the structure-activity relationship among antiviral N-quinolin-4-yl-N'-benzylidenehydrazine (II) derivatives. The total hydrophilicity of substituents in the benzylidene moiety along with 4 indicator variables is found to significantly (p less than 0.001) discriminate 25 inactive congeners of (II) from 28 active congeners with more than 80% posterior classification ratio. The predictive stability of the discriminant functions is established by the leave-one-out procedure. In the light of the posterior probabilities of assignment calculated from these functions it is observed that ethoxy group at position 7 and methoxy group at positions 8 and 6 in the quinoline system favour activity while a methoxy group at ortho or para position in the phenyl ring decreases activity. In view of the finer classification within the active class the three-group analysis is also performed using LDA and the adaptive-least-squares techniques.

Antiviral Agents↗
PubMed1982

Quantitative structure-activity relationships. IV. Use of molecular negentropy as structural parameter.

Molecular negentropy, the measure of information content in a molecule, was found to be a useful parameter for quantitation of the structures of molecules eliciting some non-specific biological response. Quantitative correlations were obtained between negentropy of a variety of molecules and their 1. toxic effects on the Madison 517 fungus, 2. narcotic action on arenicola larvae, 3. inhibiting activity on tortoise heart and 4. narcotic effect of frog heart.

Animals↗
PubMedDate not supplied

A quantitative structure-toxicity relationships model for the dermal sensitization guinea pig maximization assay.

We have developed quantitative structure-toxicity relationship (QSTR) models for assessing dermal sensitization using guinea pig maximization test (GPMT) results. The models are derived from 315 carefully evaluated chemicals. There are two models, one for aromatics (excluding one-benzene-ring compounds), and the other for aliphatics and one-benzene-ring compounds. For sensitizers, the models can resolve whether they are weak/moderate or severe sensitizers. The statistical methodology, based on linear discriminant analysis, incorporates an optimum prediction space (OPS) algorithm. This algorithm ensures that the QSTR model will be used only to make predictions on query structures which fall within its domain. Calculation of the similarities between a query structure and the database compounds from which the applicable model was developed are used to validate each skin sensitization assessment. The cross-validated specificity of the equations ranges between 81 and 91%, and the sensitivity between 85 and 95%. For an independent test set, specificity is 79%, and sensitivity 82%.

Administration, Topical↗
PubMedDate not supplied

Assessment of n-octanol/water partition coefficient: when is the assessment reliable?

A model, VLOGP, has been developed for assessment of n-octanol/water partition coefficient, log P, of chemicals from their structures. Unlike group contribution methods, VLOGP is based on linear free energy relationship (LFER) approach and employs information-rich electrotopological structure quantifiers derived solely from molecular topology. VLOGP, a robust and cross-validated model derived from accurately measured experimental log P values of 6675 diverse chemicals, has a coefficient of determination, R2, of 0.986 and a standard error of estimate of 0.20. When applied to the training set, the largest deviation observed between experimental and calculated log P was 0.42. VLOGP is different from other log P predictors in that its application domain, called Optimum Prediction Space (OPS), has been quantitatively defined, i.e., structures to which the model should not be applied for predicting log P can be identified. A computer-assisted implementation of this model within HDi's toxicity assessment software package, TOPKAT 3.0, automatically checks whether the submitted structure is inside the OPS or not. VLOGP was applied to a set of 113 chemicals not included in the training set. It was observed that for the structures inside the OPS the average deviation between experimental and model-calculated log P values is 0.27, whereas the corresponding deviation for structures outside the OPS is 1.35. This demonstrates the necessity of identifying the structures to which a model is not applicable before accepting a model-based predicted log P value. For a set of 47 nucleosides, the performance of VLOGP was compared with that of four published log P predictors; a standard deviation of 0.33 was obtained with VLOGP, whereas the standard deviation from other log P predictors ranged between 0.46 and 1.20.

1-Octanol↗