Correlation of Noncancer Benchmark Doses in Short- and Long-Term Rodent Bioassays

This study investigated whether, in the absence of chronic noncancer toxicity data, short-term noncancer toxicity data can be used to predict chronic toxicity effect levels by focusing on the dose-response relationship instead of a critical effect. Data from National Toxicology Program (NTP) technical reports have been extracted and modeled using the Environmental Protection Agency's Benchmark Dose Software. Best-fit, minimum benchmark dose (BMD), and benchmark dose lower limits (BMDLs) have been modeled for all NTP pathologist identified significant nonneoplastic lesions, final mean body weight, and mean organ weight of 41 chemicals tested by NTP between 2000 and 2012. Models were then developed at the chemical level using orthogonal regression techniques to predict chronic (two years) noncancer health effect levels using the results of the short-term (three months) toxicity data. The findings indicate that short-term animal studies may reasonably provide a quantitative estimate of a chronic BMD or BMDL. This can allow for faster development of human health toxicity values for risk assessment for chemicals that lack chronic toxicity data.

Challenges for computational structure-activity modelling for predicting chemical toxicity: future improvements?

INTRODUCTION

Structure-activity modelling for predicting toxicology as a discipline is now 50 years old, and great strides have been taken in developing methods for the physicochemical analysis of molecules and their toxicity evaluation, both essential stages in modelling. Computational toxicology also has huge potential for speeding up the screening and prioritisation of chemicals for further testing and for reducing the numbers of expensive and time-consuming conventional tests. Yet, the realisation of this potential has been largely stifled by many problems inherent in developing and validating new structure-activity models of toxicity.

AREAS COVERED

Problems of computational toxicology discussed include i) the use of inappropriate molecular descriptors and tools that are not transparent; ii) the undetected existence of chemicals that cause large changes in toxicity with only small differences in molecular structure (causing 'activity cliffs' in the structure-activity landscape); iii) spurious correlations between structure and activity; iv) lack of quality control of toxicity data; v) difficulties in determining predictivity for novel chemicals; and vi) an over-reliance on complex mathematics and statistics.

EXPERT OPINION

Greater emphasis needs to be placed on i) the selection of training and test sets of chemicals to enable both internal and external validation of models to be undertaken for more accurate assessment of model predictivity and ii) the use of recently developed techniques for characterising structure-activity landscapes.

Unified drug–target interaction thermodynamic Markov model using stochastic entropies to predict multiple drugs side effects

Most of present molecular descriptors consider just the molecular structure. In the present article we pretend extending the use of Markov chain (MC) models to define novel molecular descriptors, which consider in addition other parameters like target site or toxic effect. Specifically, this molecular descriptor takes into consideration not only the molecular structure but the specific system the drug affects too. Herein, it is developed a general Markov model that describes 21 different drugs side effects grouped in 10 affected biological systems for 193 drugs, being 311 cases finally. The data were processed by linear discriminant analysis (LDA) classifying drugs according to their specific side effects, forward stepwise was fixed as strategy for variables selection. The average percentage of good classification and number of compounds used in the training/predicting sets were 92.6/91.7% for cardiovascular manifestation (25 out of 27)/(18 out of 20); 89.3/83.9% for dermal manifestations (25 out of 18)/(18 out of 21); 88.9/88.9% for endocrine manifestations (16 out of 18)/(12 out of 14); 88.9/88.2% for psychiatric manifestations (32 out of 36)/(24 out of 27); 88.5/85.6% for systemic phenomena (23 out of 26)/(17 out of 20); 85.7/91.7% for gastrointestinal manifestations (36 out of 42)/(29 out of 32); 83.3/79.2% for metabolic manifestations (15 out of 18)/(11 out of 14); 81.8/78.0% for neurological manifestations (27 out of 33)/(20 out of 25); 75.0/74.0% for hematological manifestations (36 out of 48)/(27 out of 36) and 74.3/72.8% for breathing manifestations (26 out of 35)/(19 out of 26). Finally, application of back-projection analysis (BPA) provides physic interpretation in structural terms through molecular graphics of the toxic effects predicted with these QSTR models. This article develops a mathematical model that encompasses a large number of drugs side effects grouped in specifics systems using stochastic entropies of interaction (Thetak (j)) by the first time.

Quantitative structure-activity relationships (QSARs) within the cytochrome P450 system: QSARs describing substrate binding, inhibition and induction of P450s

Quantitative structure-activity relationships (QSARs) within substrates, inducers and inhibitors of cytochromes P450 involved in xenobiotic metabolism are reported, together with QSARs associated with induction, inhibition and metabolic rate. The importance of frontier orbitals and shape descriptors, such as planarity (estimated by the area/depth(2) parameter) and rectangularity (estimated by the length/width parameter) is discussed, particularly in the context of the COMPACT system which discriminates between several P450 families associated with the activation and detoxication of xenobiotics. The use of parameters, particularly those derived from homology modelling of mammalian (especially human) P450s that are involved in exogenous metabolism, in generating QSARs for P450 substrates is discussed in the context of explaining differences in the binding affinities of human P450 substrates which are pharmacologically active.

Quantitative structure-toxicity relationships using TOPS-MODE. 3. Structural factors influencing the permeability of commercial solvents through living human skin

The permeability of a series of 12 commercial solvents through living human skin were studied by using a topological sub-structural approach (TOPS-MODE). We first analyzed the influence of several physicochemical parameters used in describing the skin permeability of the solvents. No single significant relationship was found between any of these physicochemical properties and the permeability of the solvents. A QSAR model using TOPS-MODE descriptors was obtained and validated. This model accounted for more than 95% of the variance in the experimental permeability of these solvents. Using the derived model, the structural factors responsible for the permeability of this series of solvents through living human skin were identified. Methyl groups bonded to heteroatoms or to CH2 groups resulted in the greatest contributions to skin permeability and these groups were considered to be "permeability enhancers". In contrast, groups of the type X = O (X = S, C) were found to be "permeability inhibitors" because they possessed negative contributions to the logarithm of permeability in all of the studied solvents. Drawing on the idea of permeability "enhancers" and "inhibitors", we hypothesized that the solvents needed to orientate themselves in front of the stratum corneum layer first before penetrating through the skin.

Novel local (fragment-based) topological molecular descriptors for QSpr/QSAR and molecular design

Novel molecular descriptors based on local spectral moments of the bond matrix are defined. Mathematical expressions relating bond moments to linear combinations of structural fragments are derived. The novel descriptors are used to describe boiling points of alcohols producing a good QSPR model accounting for more than 98% of variance. A quantitative structure-reactivity model is obtained to predict the specific rate constant (log k) of the nucleophilic addition of mercaptoacetic acid to 2-furylethylene derivatives. The model accounts for more than 96% of the variance in log k. Two other models were also obtained by using molecular connectivity indices and total spectral moments of the bond matrix that account for <84% of the variance in this reactivity index. A model based on quantum chemical descriptors accounts for the same variance than that obtained with bond moments. The model based on local moments permitted to compute the contribution of different structural fragments to the reactivity, and a good relationship (r = 0.98) was obtained with these group contributions with Hammett sigma(p) constants for 21 groups.

Toward the design of chemical libraries for mass screening biased against mutagenic compounds

The ability to develop a chemical into a drug depends on multiple factors. Beyond potency and selectivity, ADME/PK and the toxicological profile of the compound play a significant role in its evaluation as a candidate for development. Those factors are being brought into bear earlier in the discovery process and even into the design of libraries for screening. The purpose of our study is the comparative analysis of simple physical characteristics of compounds that have been reported to be mutagens and nonmutagenic ones. The analysis of differences can lead to the development of knowledge-based biases in the libraries designed for massive screening. For each of four Salmonella strains, TA-98, TA-100, TA-1535, and TA-1537, an analysis of the statistical significance of the deviance of the averages for a number of global properties was carried out. The properties studied included parameters, such as topological indices, and bit strings representing the presence or absence of certain chemical moieties. The results suggest that mutagens display a larger number of hydrogen bond acceptor centers for most strains. Moreover, the use of bit strings points to the importance of certain molecular fragments, such a nitro groups, for the outcome of a mutagenicity study. Development of multivariate models based on global molecular properties or bit strings point to a small advantage of the latter for the prediction of mutagenicity. The benefits of the bit strings are in accord with the use of fragment-based approaches for the prediction of carcinogenicity and mutagenicity in methods described in the literature.

Indications for the involvement of a CYP3A-like iso-enzyme in the metabolism of chlorobornane (Toxaphene) congeners in seals from inhibition studies with liver microsomes

The different isoforms of the cytochrome P450 (CYP) system can metabolise a suite of classes of lipophilic, anthropogenic compounds. The bioaccumulative potential as well as the toxicity of xenobiotics may be significantly altered in the process. To compare the metabolic ability of different wildlife species, it is important to identify the different iso-enzymes of CYP, which are responsible for the metabolism of different classes of compounds. This can be achieved with in vitro incubation assays. In the present study, preparations of hepatic microsomes of a harbour seal (Phoca vitulina) and a grey seal (Halichoerus grypus) demonstrated that the chlorobornane (CHB) congeners CHB-32 and -62 were metabolised enzymatically to their hydroxylated derivatives. These derivatives were partially characterised by their NCI mass-spectra. Inhibition studies were carried out to identify the specific CYP isoform(s) responsible for the metabolism of CHB-32 and -62. Ketoconazole has been shown to inhibit CYP3A enzymes in human and rat studies. In this study, ketoconazole caused concentration-dependent inhibition of metabolism of CHB-32 and -62, reaching 80% at the 1.0 microM treatment level. Ellipticine (1.0 microM), which has been shown to inhibit CYP1A1/2, also inhibited CHB-32 and -62 metabolism in the microsomes of grey seal, but to a much lower degree of less than 10 and 24%, respectively. In the same experiment the metabolism of 4,4'-dichlorobiphenyl was already inhibited 70% by ellipticine treatment at the same concentration. This non-ortho substituted PCB congener can easily attain a planar molecular configuration, and therefore served as a model CYP1A substrate. Inhibition of chlorobornane metabolism was not observed after the addition of goat anti-rat CYP2B antibodies or Aldrin, which is a model CYP2B substrate in rat. Cautious interpretation is advised for results obtained with so-called selective competitive inhibitors. Regardless, these studies indicated for the first time the possible involvement a CYP3A isoform in the mediation of chlorobornane metabolism in seals. The immunochemical cross-reactivity of mouse, rabbit or sheep anti-rat antibodies in the hepatic microsomes of harbour seal confirmed the presence of CYP1A1/2, CYP1A1, CYP2B1/2, CYP3A and CYP4A isoenzymes. Enantioselective metabolism by the microsomes of harbour seal was observed for both CHB-32 and -62. Stereochemical preferences of biotransformation enzymes can have an influence on the environmental distribution of both enantiomers of optically active compounds.

Quantitative structure-toxicity relationships using TOPS-MODE. 2. Neurotoxicity of a non-congeneric series of solvents

Neurotoxicities of a series of solvents in rats and mice have been modeled by means of the TOPS-MODE approach. Two quantitative structure-toxicity relationship (QSTR) models were obtained explaining more than 80% of the variance in the experimental values of neurotoxicity of 45 solvents. Only one compound was detected as statistical outlier for these models. In contrast, previous models explained less than 60% of the variance in this property for 44 solvents. Finally, the contributions to neurotoxicity in rats and mice for a series of structural fragments were found. Structural characteristics of chlorinated fragments responsible for their different neurotoxicities were analyzed. The differences in neurotoxic behavior of some fragments in rats and mice were also analyzed, which could give insights on the toxicological mechanism of action of solvents studied.

Quantitative structure-based modeling applied to characterization and prediction of chemical toxicity

Quantitative modeling methods, relating aspects of chemical structure to biological activity, have long been applied to the prediction and characterization of chemical toxicity. The early linear free-energy approaches of Hansch and Free Wilson provided a fundamental scientific framework for the quantitative correlation of chemical structure with biological activity and spurred many developments in the field of quantitative structure-activity relationships (QSARs). In addition to modeling of chemical toxicity, these methods have been extensively applied to modeling of medicinal properties of chemicals. However, there are important differences in the nature and objectives of these two applications, which have led to the evolution of different modeling approaches (namely, the need for treating sets of noncongeneric toxic compounds). In this paper are discussed those approaches to chemical toxicity that have taken a more "personalized" configuration and have undergone implementation into software programs able to perform the various steps of the assessment of the hazard posed by the chemicals. These models focus both on a variety of toxicological endpoints and on key elements of toxicity mechanisms, such as metabolism.

Computational predictive programs (expert systems) in toxicology

The increasing number of pollutants in the environment raises the problem of the toxicological risk evaluation of these chemicals. Several so called expert systems (ES) have been claimed to be able to predict toxicity of certain chemical structures. Different approaches are currently used for these ES, based on explicit rules derived from the knowledge of human experts that compiled lists of toxic moieties for instance in the case of programs called HazardExpert and DEREK or relying on statistical approaches, as in the CASE and TOPKAT programs. Here we describe and compare these and other intelligent computer programs because of their utility in obtaining at least a first rough indication of the potential toxic activity of chemicals.

Quantitative structure-activity relationships and COMPACT analysis of a series of food mutagens

Quantitative structure-activity relationships between chemical structure and Ames mutagenicity for a group of 24 food mutagens, including 17 cooked-food heterocyclic amines, have been determined. For the TA98 strain of Salmonella typhimurium (frameshift mutagens) the best correlation of mutagenicity is with molecular diameter (R = 0.91), while for the TA100 strain (base-pair mutations) the best correlation is with delta E, the energy difference between the lowest unoccupied and highest occupied molecular orbitals. High mutagenicity is related to high values of molecular diameter, hence to planarity and to high values of the COMPACT ratio ([area/depth2]/delta E). High mutagenicity is also related to low values of delta E. Consequently, highly mutagenic and potentially carcinogenic food chemicals can readily be identified as substrates of cytochrome P4501 (CYP1) and may therefore be detected by the COMPACT procedure. Highly mutagenic compounds also exhibit high values of dipole moment.

Role of computational chemistry in support of hazard identification (ID): mechanism-based SARs

A mechanism-based structure-activity relationship (SAR) study examines the structural basis for a chemical/biological activity by targeting a single or a few stages in a postulated mechanism of action. Computational chemistry approaches provide a valuable complement to experiment for probing such associations, but require a highly focused viewpoint that neglects much of the full biological and chemical interaction problem. Research questions are formulated in terms of fundamental structure and reactivity properties and are designed to test key assumptions of a postulated mechanism of activity. The results of such studies can aid in the generation of new hypotheses, suggest new experiments, and provide scientific rationale for extrapolation in hazard identification (ID). Toxicologists and computational chemists bring very different, yet complementary viewpoints, approaches, and expertise to bear on the hazard ID problem. However, improved communication and interaction between these two groups is needed to most productively address hazard ID issues.

Safety evaluations of food chemicals by "COMPACT". 1. A study of some acyclic terpenes

A group of 19 acyclic terpenes have been evaluated for potential toxicity/carcinogenicity by molecular orbital determinations of their spatial and electronic parameters, and hence prediction of their metabolic activation or detoxication by the cytochrome P-450 (CYP) superfamily of mixed-function oxidase enzymes. Previous studies have characterized the spatial dimensions of the CYP1A1, 1A2 and 2E1 enzymes, which are known to activate mutagens and carcinogens and to be involved in other mechanisms of toxicity. None of the terpenes was found to have shape or electronic parameters appropriate for metabolic activation by CYP1A1 or 1A2, and hence they are unlikely to be carcinogenic or mutagenic. Furthermore, none of these chemicals had spatial parameters critical for substrates of CYP2E, and they are therefore unlikely to induce the formation of reactive oxygen species (ROS) or to initiate or promote malignancy or toxicity by mechanisms involving ROS. However, citral, and others of these terpenes, are known to undergo metabolism to carboxylic acids that may induce CYP4, and are therefore possible inducers of hepatic peroxisomal proliferation at high dosage, which may have implications for possible hepatotoxicity.

The use of computers in the safety evaluation of drugs and other chemicals

The toxicity and carcinogenicity of drugs and other chemicals is, in most cases, mediated by highly reactive intermediates which are generated following metabolism catalysed by the enzymic apparatus of the exposed organisms. These reactive intermediates readily interact covalently with vital cellular components to provoke toxicity and carcinogenicity. The ubiquitous cytochrome P450-dependent mixed-function oxidases are the most important enzyme system in the activation of chemicals. This enzyme system comprises a number of families, each of which contains one or more subfamilies. The CYPIA and CYP2E subfamilies are the most closely associated with the production of reactive intermediates and, consequently, the manifestation of toxicity and carcinogenicity. A computer based molecular structure procedure (COMPACT) has been developed which, via a calculation of the molecular and electronic structure of the chemical, determines whether the chemical will interact with either of these two cytochrome P450 subfamilies and hence be metabolised to form reactive intermediates that manifest toxicity. As the basal levels of these two subfamilies are generally low, the ability of a chemical to induce them selectively, on repeated administration, is an important determinant of its toxic and carcinogenic potential. This inductive capability may be determined in short-term studies (ENACT) using only a small number of animals. Thus the combination of COMPACT and ENACT provides a rapid and inexpensive means for the preliminary screening of chemicals for toxicity and carcinogenicity before undertaking the long-term and expensive rodent lifetime bioassays.