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M T D Cronin

Publications and source records attributed to M T D Cronin.

17 recordsLinked to original sources
PubMed2006

A conceptual framework for predicting the toxicity of reactive chemicals: modeling soft electrophilicity.

Although the literature is replete with QSAR models developed for many toxic effects caused by reversible chemical interactions, the development of QSARs for the toxic effects of reactive chemicals lacks a consistent approach. While limitations exit, an appropriate starting-point for modeling reactive toxicity is the applicability of the general rules of organic chemical reactions and the association of these reactions to cellular targets of importance in toxicology. The identification of plausible "molecular initiating events" based on covalent reactions with nucleophiles in proteins and DNA provides the unifying concept for a framework for reactive toxicity. This paper outlines the proposed framework for reactive toxicity. Empirical measures of the chemical reactivity of xenobiotics with a model nucleophile (thiol) are used to simulate the relative rates at which a reactive chemical is likely to bind irreversibly to cellular targets. These measures of intrinsic reactivity serve as correlates to a variety of toxic effects; what's more they appear to be more appropriate endpoints for QSAR modeling than the toxicity endpoints themselves.

Amino Acids↗
PubMed2005

Environmental agent susceptibility assessment using existing and novel biomarkers as rapid noninvasive testing methods.

This study is part of a project aimed at developing and validating novel noninvasive methods for the detection of biomarkers of endocrine disrupters (EDs) directly in the mucus of aquatic species, to identify novel functional biomarker(s) for EDs, and to verify their applicability for field studies. The multidisciplinary approach chosen aims at the development of an integrated testing strategy utilizing in vitro protocols to identify water and sediment fractions with potential endocrine-disrupting activity; the identification, characterization, and measurement of new biomarker(s) for EDs; the development and validation of a dipstick-based test method; and the development of (computer-assisted) predictive models. Some results of the first year of the project are presented here.

Animals↗
PubMed2005

Metals, toxicity and oxidative stress.

Metal-induced toxicity and carcinogenicity, with an emphasis on the generation and role of reactive oxygen and nitrogen species, is reviewed. Metal-mediated formation of free radicals causes various modifications to DNA bases, enhanced lipid peroxidation, and altered calcium and sulfhydryl homeostasis. Lipid peroxides, formed by the attack of radicals on polyunsaturated fatty acid residues of phospholipids, can further react with redox metals finally producing mutagenic and carcinogenic malondialdehyde, 4-hydroxynonenal and other exocyclic DNA adducts (etheno and/or propano adducts). Whilst iron (Fe), copper (Cu), chromium (Cr), vanadium (V) and cobalt (Co) undergo redox-cycling reactions, for a second group of metals, mercury (Hg), cadmium (Cd) and nickel (Ni), the primary route for their toxicity is depletion of glutathione and bonding to sulfhydryl groups of proteins. Arsenic (As) is thought to bind directly to critical thiols, however, other mechanisms, involving formation of hydrogen peroxide under physiological conditions, have been proposed. The unifying factor in determining toxicity and carcinogenicity for all these metals is the generation of reactive oxygen and nitrogen species. Common mechanisms involving the Fenton reaction, generation of the superoxide radical and the hydroxyl radical appear to be involved for iron, copper, chromium, vanadium and cobalt primarily associated with mitochondria, microsomes and peroxisomes. However, a recent discovery that the upper limit of "free pools" of copper is far less than a single atom per cell casts serious doubt on the in vivo role of copper in Fenton-like generation of free radicals. Nitric oxide (NO) seems to be involved in arsenite-induced DNA damage and pyrimidine excision inhibition. Various studies have confirmed that metals activate signalling pathways and the carcinogenic effect of metals has been related to activation of mainly redox-sensitive transcription factors, involving NF-kappaB, AP-1 and p53. Antioxidants (both enzymatic and non-enzymatic) provide protection against deleterious metal-mediated free radical attacks. Vitamin E and melatonin can prevent the majority of metal-mediated (iron, copper, cadmium) damage both in vitro systems and in metal-loaded animals. Toxicity studies involving chromium have shown that the protective effect of vitamin E against lipid peroxidation may be associated rather with the level of non-enzymatic antioxidants than the activity of enzymatic antioxidants. However, a very recent epidemiological study has shown that a daily intake of vitamin E of more than 400 IU increases the risk of death and should be avoided. While previous studies have proposed a deleterious pro-oxidant effect of vitamin C (ascorbate) in the presence of iron (or copper), recent results have shown that even in the presence of redox-active iron (or copper) and hydrogen peroxide, ascorbate acts as an antioxidant that prevents lipid peroxidation and does not promote protein oxidation in humans in vitro. Experimental results have also shown a link between vanadium and oxidative stress in the etiology of diabetes. The impact of zinc (Zn) on the immune system, the ability of zinc to act as an antioxidant in order to reduce oxidative stress and the neuroprotective and neurodegenerative role of zinc (and copper) in the etiology of Alzheimer's disease is also discussed. This review summarizes recent findings in the metal-induced formation of free radicals and the role of oxidative stress in the carcinogenicity and toxicity of metals.

Animals↗
PubMed2004

QSARS for toxicity to the bacterium Sinorhizobium meliloti.

In the present study, structure-activity relationship (QSAR) models for the prediction of the toxicity to the bacterium Sinorhizobium meliloti have been developed, based on a data set of 140 compounds. The data set is highly heterogeneous both in terms of chemistry and mechanisms of toxic action. For deriving QSARs, chemicals were divided into groups according to mechanism of action and chemical structure. The QSARs derived are considered to be of moderate statistical quality. A baseline effect (relationship between the toxicity and logP), which can be related to non-polar narcosis, was observed. To explain toxicity greater than the baseline toxicity, other structural descriptors were used. The development of models for non-polar and polar narcosis had some success. It appeared that the toxicity of compounds acting by more specific mechanisms of toxic action is difficult to predict. A global QSAR was also developed, which had square of the correlation coefficient r2 = 0.53. A QSAR with reasonable statistical parameters was developed for the aliphatic compounds in the data set (r2 = 0.83). QSARs could not be obtained for the aromatic compounds as a group.

Forecasting↗
PubMed2003

Selection of data sets for QSARs: analyses of Tetrahymena toxicity from aromatic compounds.

The aim of this investigation was to develop a strategy for the formulation of a valid ecotoxicological-based QSAR while, at the same time, minimizing the required number of toxicological data points. Two chemical selection approaches-distance-based optimality and K Nearest Neighbor (KNN), were used to examine the impact of the number of compounds used in the training and testing phases of QSAR development (i.e. diversity and representivity, respectively) on the predictivity (i.e. external validation) of the QSAR. Regression-based QSARs for the ectotoxic potency for population growth impairment of aromatic compounds (benzenes) to the aquatic ciliate Tetrahymena pyriformis were developed based on descriptors for chemical hydrophobicity and electrophilicity. A ratio of one compound in the training set to three in the test set was applied. The results indicate that from a known chemical universe, in this case 385 derivatives, robust QSARs of equal quality may be developed from a small number of diverse compounds, validated by a representative test set. As a conservative recommendation it is suggested that there should be a minimum of 10 observations for each variable in a QSAR.

Animals↗
PubMed2003

Partial least squares modelling of the acute toxicity of aliphatic compounds to Tetrahymena pyriformis.

The aim of this study was to evaluate a multivariate statistical model, utilising Partial Least Squares (PLS) analysis, for the prediction of the acute toxicity of aliphatic chemicals to the ciliate Tetrahymena pyriformis. A model was developed that was capable of making a prediction regardless the mechanism of toxic action. The toxicity of 476 compounds, possessing different mechanisms of toxic action was considered. A set of 74 descriptors, including the octanol-water partition coefficient, molecular-orbital descriptors, geometrical, topological and connectivity indices, was generated. A three-component, eight-descriptor PLS model was developed. It was validated by a Y-permutation test and by simulation of external prediction for complementary subsets. A comparison with existing class or mechanism-based models, derived on the same data set, was made.

Animals↗
PubMed2002

Quantitative structure-permeability relationships (QSPRs) for percutaneous absorption.

Quantitative structure-permeability relationships (QSPRs) have been derived by many researchers to model the passive, diffusion-controlled, percutaneous penetration of exogenous chemicals. Most of these relationships are based on experimental data from the published literature. They indicate that molecular size (as molecular weight) and hydrophobicity (as the logarithm of the octanol-water partition coefficient; log k(ow)) are the main determinants of transdermal penetration. This article reviews the current state of the art in QSPRs for absorption of chemicals through the skin, and where this technology can be exploited in future research. The main shortfalls in QSPR models result from inconsistency and error of the experimental values used to derive them. This is probably caused by the manner in which they employ data from a variety of sources and, in some cases, slightly different experimental protocols. Further, most current models are based on data generated from either aqueous or ethanolic solution, where each penetrant is present at its saturated solubility or a fraction of its saturated solubility. No models currently account for the influences of formulation upon percutaneous penetration. Current QSPR models provide a significant tool for assessing the percutaneous penetration of chemicals. They may be important in determining the bioavailability of a range of topically applied exogenous chemicals, and in issues of dermal toxicology and risk assessment. However, their current use may be limited by their lack of applicability across different formulation types. As a consequence, their true value may be to make predictions within specific formulation types, as opposed to a general model based on a range of formulation types. In addition, the endpoint of models may be inappropriate for specific applications other than the systemic delivery of topically applied chemicals.

Administration, Cutaneous↗
PubMed2002

The importance of hydrophobicity and electrophilicity descriptors in mechanistically-based QSARs for toxicological endpoints.

Quantitative structure-activity relationship (QSAR) analysis of four toxicological data sets is described. The toxicological data include three data sets retrieved from the literature (the toxic and metabolic effects of 23 aliphatic alcohols on the perfused rat liver; the toxicity of 21 pyridines to mice; the lethality of 55 halogenated hydrocarbons to the mould Aspergillus nidulans). In addition, the toxicity of 13 mono- and di-substituted nitrobenzenes in a 15 min assay using the alga Chlorella vulgaris was analysed. QSARs were developed successfully using descriptors to describe uptake in the organism (i.e. hydrophobicity as quantified by the logarithm of the octanol-water partition coefficient, log P) and reactivity at the site of action (i.e. electrophilicity as quantified by the energy of the lowest unoccupied molecular orbital, E(LUMO)). A further parameter describing molecular branching as also required to model the data for the aliphatic alcohols. The results demonstrate that mechanistically based QSARs can be developed for these diverse endpoints which are, in terms of statistical quality as good as, if not better, than QSARs based on less mechanistically interpretable descriptors.

Animals↗
PubMed2002

The effect of precision of molecular orbital descriptors on toxicity modeling of selected pyridines.

The response-surface approach to QSARs attempts to model toxic potency of diverse groups of chemicals while avoiding problems associated with the identification of the mechanism of toxic action or specific chemical class often associated with other approaches. However, while hydrophobicity-dependent, simple regression QSARs derived for congeneric series of organic compounds typically have coefficients of determination greater than 0.90, more heterogeneous multiple regression QSARs exhibit typically 10-15% more unexplained variability. One difference between these approaches is the use of a quantum chemical (QC) descriptor, particularly molecular orbital (MO) energy values such as the energy of the lowest unoccupied molecular orbital (E(LUMO)). The reduced statistical fit exhibited by QSAR models, which include these QC-MO descriptors, could be a result of the variability inherent in the calculation of these descriptors. The present investigation with a structurally and mechanistically diverse set of pyridines revealed that variability is associated with the calculation of the MO descriptor E(LUMO) both between selected Hamiltonians and selected software packages. However, this variability in no way affects the statistical significance of QSARs for toxicity using these values. Specifically, the E(LUMO) values calculated with the PM3 and AM1 Hamiltonians in the two software packages were highly related. There was no relationship between molecular complexity or chemical reactivity and increased differences in individual ELUMO values as described by the standard errors of the mean. Although nine appeared to be the number of calculations, which best minimizes the standard error in energy values relative to computational costs; this minimization did not alter the statistics of the QSARs derived with single vs. mean E(LUMO) values. While the energy of the highest occupied molecular orbital (E(HOMO)) values were not used in the modeling of toxicity, a comparison of these values revealed greater variability between the Hamiltonians and software packages than observed for ELUMO values. Examination of the magnitudes of standard error of the E(HOMO) values in connection to structural features or reactivity likewise revealed no trends.

Forecasting↗
PubMedDate not supplied

Evaluation of qsars for ecotoxicity: a method for assigning quality and confidence.

Validation of a quantitative structure-activity relationship (QSAR) is now considered as an integral part of its development. Assessment of the quality of a QSAR and the confidence that may be placed in predictions from it are vital to any validation procedure. A number of terms associated with the quality of a QSAR, confidence in that QSAR, or both may be quantified. These terms include the: (1) goodness of fit of the model (r2); (2) predictivity of the model (Q2); (3) stability of the model described as the difference between fit and predictivity (Dfp); (4) number of compounds used in the training set (Nc); (5) number of descriptors used in the model (Nd); (6) range of toxicity values (Tr); (7) number of mechanisms of toxic action covered by the training set (Nm), as well as two factors associated with the biological data-confidence associated with, (8) reproducibility of the data (Rconf) and (9) confidence in the source of the data (Sconf). While all these factors may influence the quality of, and/or confidence in a particular QSAR, each varies within different limits. To enable a quantitative assessment of quality and confidence in a QSAR, the terms deemed to be important were weighed and combined to create a Confidence Index (CI): ((r2)4 x 6) x ((Q2)4 x 6) x (ln(Nc/10)) x (Tr) x (Sconf)0.5 (ln(N2d + 2)) x (ln(N2m + 2)) x ((r2)4 x 6) - ((Q2)4 x 6) + 1) x (Rconf)

Algorithms↗
PubMedDate not supplied

Prediction of hERG K+ blocking potency: application of structural knowledge.

Modelling of QT-prolongation has been performed using data for 19 structurally diverse hERG K+ channel blocking drugs taken from literature. The modelling used hydrophobicity corrected for ionisation (log D) and various 2D and 3D physico-chemical molecular descriptors. Stepwise regression produced a two parameter, interpretable and transparent QSAR with good statistical fit, including log D and the maximum diameter of molecules (Dmax). Two strategies were applied for model validation: (i) a scrambling procedure, i.e., training the total set of 19 chemicals after randomising the hERG K+ channel blocking activity data and (ii) use of external validation sets. Validation of the models showed them to be stable and statistically significant. The effect of molecular size on QT-prolongation side effect is discussed.

Anti-Arrhythmia Agents↗
PubMedDate not supplied

Qsar investigation of a large data set for fish, algae and Daphnia toxicity.

A large data set containing values for fish, algae and Daphnia toxicity for more than 2000 chemicals and mixtures was investigated. The data set was taken from the New Chemicals Data Base of the European Union [hosted by the European Chemicals Bureau, Joint Research Centre, European Commission (http://ecb.jrc.it)]. The data are submitted by industry, according to the requirements of EU Council Directive 67/548/EEC as amended for the seventh time by EU Council Directive 92/32/EEC. The toxicities of neutral chemicals, salts, metal complexes, as well as chemical mixtures were extracted. A baseline effect was demonstrated by chemicals known to act by a narcotic mechanism of action, i.e., a relationship was observed between the toxicity and the logarithm of the octanol-water partition coefficient (log P). However, the prediction of the toxicity of more reactive chemicals was found to require the use of additional descriptors.

Algorithms↗
PubMedDate not supplied

The impact of variable selection on the modelling of oestrogenicity.

Many oestrogenic chemicals exert their activity via specific interactions with the oestrogen receptor (ER). The objective of the present study was to identify significant descriptors associated with the ER binding affinities of a large and diverse set of compounds to drive quantitative structure-activity relationships (QSARs). To this end, a variety of statistical methods were employed for variable selection. These included stepwise regression and partial least squares (PLS) analyses, as well as a non-linear recursive partitioning method (Formal Inference-based Recursive Modelling). A total of 157 molecular descriptors including quantum mechanical, graph theoretical, indicator variables and log P were used in the study. Furthermore, cluster analysis of variables was performed to identify groups of descriptors representing similar molecular features. Hierarchical PLS analyses were performed, where the scores of the significant components of either PLS or principle component analysis (PCA), performed separately on each cluster, were used as the variables for the top model. This reduced the number of the variables representing the larger clusters, leading to a similar number of descriptors for each distinct molecular feature. The results showed that the most important molecular properties for stronger ER binding affinity are molecular size and shape, the presence of a phenol moiety as well as other aromatic groups, hydrophobicity and presence of double bonds. The best PLS model obtained, in terms of predictive ability, was a hierarchical PLS model. However, a rigorous validation study showed that the MLR model using descriptors selected by stepwise regression has greater predictive power than the PLS models.

Cluster Analysis↗
PubMedDate not supplied

3D QSAR investigation of the blood-brain barrier penetration of chemical compounds.

In the present study, we investigated structure-permeability relationships for the blood-brain barrier (BBB) of 16 imipramine and phenothiazine derivatives. The compounds belong to structurally related chemical classes of catamphiphiles, representatives of which have previously been investigated for membrane activity and ability to overcome multidrug resistance (MDR) in tumour cells. These studies show that phenothiazines and structurally related drugs (imipramines, thioxanthenes, acridines) interact with membrane phospholipids, and additionally inhibit the MDR transport P-glycoprotein. This study aimed to identify common 3D structural characteristics of these compounds related to their mechanism of transport across the BBB. For this purpose Genetic Algorithm Similarity Programme (GASP), Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA) were applied. The results demonstrate the importance of the spatial distribution of molecular hydrophobicity for the BBB penetration of the investigated compounds. It suggests that the compounds should follow a specific profile of two hydrophobic and one hydrophilic centres in a particular space configuration, for optimal BBB penetration.

Blood-Brain Barrier↗
PubMedDate not supplied

Structure-based methods for the prediction of the dominant P450 enzyme in human drug biotransformation: consideration of CYP3A4, CYP2C9, CYP2D6.

Metabolic drug-drug interactions are receiving more and more attention from the in silico community. Early prediction of such interactions would not only improve drug safety but also contribute to make drug design more predictable and rational. The aim of this study was to build a simple and interpretable model for the determination of the P450 enzyme predominantly responsible for a drug's metabolism. The P450 enzymes taken into consideration were CYP3A4, CYP2D6 and CYP2C9. Physico-chemical descriptors and structural descriptors for 96 currently marketed drugs were submitted to statistical analysis using the formal inference-based recursive modelling (FIRM) method, a form of recursive partitioning. Generally accepted knowledge on metabolism by these enzymes was also used to construct a hierarchical decision tree. Robust methods of variable selection using recursive partitioning were utilised. The descriptive ability of the resulting hierarchical model is very satisfactory, with 94% of the compounds correctly classified.

Aryl Hydrocarbon Hydroxylases↗