Development of quantitative structure–activity relationship (QSAR) models to predict the carcinogenic potency of chemicals

Dimensions of elongate mineral particles and cancer: A review

CONTEXT

Based on a decade-long exploration, dimensions of elongate mineral particles are implicated as a pivotal component of their carcinogenic potency. This paper summarizes current understanding of the discovered relationships and their importance to the protection of public health.

OBJECTIVES

To demonstrate the relationships between cancer risk and dimensions (length, width, and other derivative characteristics) of mineral fibers by comparing the results and conclusions of previously published studies with newly published information.

METHODS

A database including 59 datasets comprising 341,949 records were utilized to characterize dimensions of elongate particles. The descriptive statistics, correlation and regression analysis, combined with Monte Carlo simulation, were used to select dimensional characteristics most relevant for mesothelioma and lung cancer risk prediction.

RESULTS

The highest correlation between mesothelioma potency factor and weight fraction of size categories is achieved for fibers with lengths >5.6 μm and widths ≤0.26 μm (R = 0.94, P < 0.02); no statistically significant potency was found for lengths <5 μm. These results are consistent with early published estimations, though are derived from a different approach. For combinations of amphiboles and chrysotile (with a consideration of a correction factor between mineral classes), the potency factors correlated most highly with a fraction of fibers longer than 5 μm and thinner than 0.2 μm for mesothelioma, and longer than 5 μm and thinner than 0.3 μm for lung cancer. Because the proportion of long, thin particles in asbestiform vs. non-asbestiform dusts is higher, the cancer potencies of the former are predicted at a significantly higher level. The analysis of particle dimensionality in human lung burden demonstrates positive selection for thinner fibers (especially for amosite and crocidolite) and prevailing fraction of asbestiform habit.

CONCLUSION

Dimensions of mineral fibers can be confirmed as one of the main drivers of their carcinogenicity. The width of fibers emerges as a primary potency predictor, and fibers of all widths with lengths shorter than 5 μm seem to be non-impactful for cancer risk. The mineral dust with a fibrous component is primarily carcinogenic if it contains amphibole fibers longer than 5 μm and thinner than 0.25 μm.

Identification, isolation, structural characterization, in silico toxicity prediction and in vitro cytotoxicity assay of simeprevir acidic and oxidative degradation products

Simeprevir is a new direct-acting antiviral drug used for the treatment of chronic hepatitis C. In this work, a simple, fast and economical chromatographic method was developed for the determination of simeprevir in the presence of its acidic and oxidative degradation products. The stress studies performed herein showed that simeprevir degraded under acidic and oxidative conditions but was stable under thermal and alkaline conditions. Chromatographic separation was achieved on a reversed-phase Eclipse XDB C₁₈ column (4.6 × 150 mm, 5 μm). The mobile phase consisted of methanol-0.05 M ammonium acetate (pH 4) (90 : 10, v/v) and was used at a flow rate of 1 mL min⁻¹. The column effluent was monitored at 237 nm. The calibration curve was linear over the concentration range of 0.1–20 μg mL⁻¹. The relative standard deviations for the intra-day and inter-day precision were less than 2%, and good percentage recoveries that met the acceptance criteria of the International Conference on Harmonization (ICH) guidelines were obtained. The robustness was assessed using the Plackett–Burman design. The simeprevir degradation products were isolated by flash chromatography and confirmed by ¹H NMR and LC-MS/MS techniques. The fully validated chromatographic method can be applied as a stability-indicating method for simeprevir and for routine analysis during quality control. Additionally, in silico toxicity prediction of the degradation products demonstrated a hepatotoxicity alert for DP 1, DP 2, DP 4 and DP 5 and a carcinogenicity alert for DP 3. In view of safety aspects, an in vitro cytotoxicity assay was carried out for simeprevir degradation products. They were found to be non-toxic in vitro at the tested concentrations.

Simeprevir is a new direct-acting antiviral drug used for the treatment of chronic hepatitis C.

CORAL: QSAR models for carcinogenicity of organic compounds for male and female rats

Quantitative structure - activity relationships (QSARs) for carcinogenicity (rats, TD₅₀) have been built up using the CORAL software. Different molecular features, which are extracted from simplified molecular input-line entry system (SMILES) serve as the basis for building up a model. Correlation weights for the molecular features are calculated by means of the Monte Carlo optimization. Using the numerical data on the correlation weights, one can calculate a model of carcinogenicity as a mathematical function of descriptors, which are sum of the corresponding correlation weights. In other words, the correlation weights provide the maximal correlation coefficient between the descriptor and carcinogenicity, for the training set. This correlation was assessed via external validation set. Finally, lists of molecular alerts in aspects of carcinogenicity for male rats and for female rats were compared and their differences were discussed.

Prediction of Biochemical Endpoints by the CORAL Software: Prejudices, Paradoxes, and Results

Quantitative structure-activity relationships (QSARs) for prediction of toxicological endpoints built up with the CORAL software are discussed. Prejudices related to these QSAR models are listed. Possible ways to improve the software are discussed.

In silico prediction of the mutagenicity of nitroaromatic compounds using a novel two-QSAR approach

Certain drugs are nitroaromatic compounds, which are potentially toxic. As such, it is of practical importance to assess and predict their mutagenic potency in the process of drug discovery. A classical quantitative structure-activity relationship (QSAR) model was developed using the linear partial least square (PLS) scheme to understand the underline mutagenic mechanism and a non-classical QSAR model was derived using the machine learning-based hierarchical support vector regression (HSVR) to predict the mutagenicity of nitroaromatic compounds based on a series of mutagenicity data (TA98-S9). It was observed that HSVR performed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical validations. A mock test designated to mimic real challenges also confirmed the better performance of HSVR. Furthermore, HSVR exhibited superiority in predictivity, generalization capabilities, consistent performance, and robustness when compared with various published predictive models. PLS, conversely, revealed some mechanistically interpretable relationships between descriptors and mutagenicity. Thus, this two-QSAR approach using the predictive HSVR and interpretable PLS models in a synergistic fashion can be adopted to facilitate drug discovery and development by designing safer drug candidates with nitroaromatic moiety.

In silico and in vitro genotoxicity evaluation of descarboxyl levofloxacin, an impurity in levofloxacin

It is important to establish the safety of impurities in drug substances or drug products. The assessment of genotoxicity of impurities and the determination of acceptable limits for genotoxic impurities was addressed in some recent guidances as a difficult issue. Descarboxyl levofloxacin is an impurity isolated from levofloxacin, which may impose a risk without associated benefit. However, there is insufficient toxic information about descarboxyl levofloxacin. This study investigated the genotoxicity of this impurity by in silico and in vitro methods. We used Derek, a commercial structure-activity relationship software package, as an in silico tool. The results showed that there was a structural alert (quinoline) in this impurity. Then, the in vitro genotoxicity of descarboxyl levofloxacin was investigated by a modified Ames test and by a chromosomal aberration test, using Chinese hamster lung (CHL) cells. Both assays were conducted in the presence or absence of S-9 mix. The results showed that the test impurity was not mutagenic in the Ames test (31.25-500 μg/plate). Whereas there was a statistically significant increase in the number of metaphase CHL cells with structural aberrations at the concentration of 1 mg/mL with S-9 mix, the aberrations rate was 7.5%. It did not significantly increase the number of structural aberration in CHL cells in the presence (at 250 and 500 μg/mL) or absence of S-9 mix. Based on these assays, descarboxyl levofloxacin could be controlled as a nongenotoxic impurity.

Establishing the level of safety concern for chemicals in food without the need for toxicity testing

There is demand for methodologies to establish levels of safety concern associated with dietary exposures to chemicals for which no toxicological data are available. In such situations, the application of in silico methods appears promising. To make safety statement requires quantitative predictions of toxicological reference points such as no observed adverse effect level and carcinogenic potency for DNA-reacting chemicals. A decision tree (DT) has been developed to aid integrating exposure information and predicted toxicological reference points obtained with quantitative structure activity relationship ((Q)SAR) software and read across techniques. The predicted toxicological values are compared with exposure to obtain margins of exposure (MoE). The size of the MoE defines the level of safety concern and should account for a number of uncertainties such as the classical interspecies and inter-individual variability as well as others determined on a case by case basis. An analysis of the uncertainties of in silico approaches together with results from case studies suggest that establishing safety concern based on application of the DT is unlikely to be significantly more uncertain than based on experimental data. The DT makes a full use of all data available, ensuring an adequate degree of conservatism. It can be used when fast decision making is required.

Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models was performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes.

Prediction of acute mammalian toxicity using QSAR methods: a case study of sulfur mustard and its breakdown products

Predicting toxicity quantitatively, using Quantitative Structure Activity Relationships (QSAR), has matured over recent years to the point that the predictions can be used to help identify missing comparison values in a substance's database. In this manuscript we investigate using the lethal dose that kills fifty percent of a test population (LD₅₀) for determining relative toxicity of a number of substances. In general, the smaller the LD₅₀ value, the more toxic the chemical, and the larger the LD₅₀ value, the lower the toxicity. When systemic toxicity and other specific toxicity data are unavailable for the chemical(s) of interest, during emergency responses, LD₅₀ values may be employed to determine the relative toxicity of a series of chemicals. In the present study, a group of chemical warfare agents and their breakdown products have been evaluated using four available rat oral QSAR LD₅₀ models. The QSAR analysis shows that the breakdown products of Sulfur Mustard (HD) are predicted to be less toxic than the parent compound as well as other known breakdown products that have known toxicities. The QSAR estimated break down products LD₅₀ values ranged from 299 mg/kg to 5,764 mg/kg. This evaluation allows for the ranking and toxicity estimation of compounds for which little toxicity information existed; thus leading to better risk decision making in the field.

Joint toxicity of alkoxyethanol mixtures: contribution of in silico applications

Exposure to chemicals occurs often as mixtures. Presented in this paper is information on alkoxyethanols and the impact they might have on human health in combination with some commonly found aliphatic and aromatic compounds. Our studies to evaluate the joint toxicity of these chemicals among themselves and in combination with other chemicals reveal a variety of possible outcomes depending on the exposure scenario. The interactions are predominantly based on metabolic pathways and are common among several solvents and organic compounds. Quantitative structure activity relationship (QSAR) analysis can be used with high confidence to identify chemicals that will interact to influence overall joint toxicity. Potential human exposure to a combination of alkoxyethanol, toluene and substituted benzenes may increase reproductive and developmental disease conditions. Inheritable gene alterations result in changes in the enzyme function in different subpopulations causing variations in quantity and/or quality of particular isoenzymes. These changes are responsible for differential metabolism of chemicals in species, genders, and life stages and are often the basis of a population's susceptibility. Unique genotypes introduced as a function of migration can alter the genetic makeup of any given population. Hence special consideration should be given to susceptible populations while conducting chemical health risk assessments.

Role of in silico genotoxicity tools in the regulatory assessment of pharmaceutical impurities

The toxicological assessment of genotoxic impurities is important in the regulatory framework for pharmaceuticals. In this context, the application of promising computational methods (e.g. Quantitative Structure-Activity Relationships (QSARs), Structure-Activity Relationships (SARs) and/or expert systems) for the evaluation of genotoxicity is needed, especially when very limited information on impurities is available. To gain an overview of how computational methods are used internationally in the regulatory assessment of pharmaceutical impurities, the current regulatory documents were reviewed. The software recommended in the guidelines (e.g. MCASE, MC4PC, Derek for Windows) or used practically by various regulatory agencies (e.g. US Food and Drug Administration, US and Danish Environmental Protection Agencies), as well as other existing programs were analysed. Both statistically based and knowledge-based (expert system) tools were analysed. The overall conclusions on the available in silico tools for genotoxicity and carcinogenicity prediction are quite optimistic, and the regulatory application of QSAR methods is constantly growing. For regulatory purposes, it is recommended that predictions of genotoxicity/carcinogenicity should be based on a battery of models, combining high-sensitivity models (low rate of false negatives) with high-specificity ones (low rate of false positives) and in vitro assays in an integrated manner.

An in silico approach for evaluating a fraction-based, risk assessment method for total petroleum hydrocarbon mixtures

Both the Massachusetts Department of Environmental Protection (MADEP) and the Total Petroleum Hydrocarbon Criteria Working Group (TPHCWG) developed fraction-based approaches for assessing human health risks posed by total petroleum hydrocarbon (TPH) mixtures in the environment. Both organizations defined TPH fractions based on their expected environmental fate and by analytical chemical methods. They derived toxicity values for selected compounds within each fraction and used these as surrogates to assess hazard or risk of exposure to the whole fractions. Membership in a TPH fraction is generally defined by the number of carbon atoms in a compound and by a compound's equivalent carbon (EC) number index, which can predict its environmental fate. Here, we systematically and objectively re-evaluate the assignment of TPH to specific fractions using comparative molecular field analysis and hierarchical clustering. The approach is transparent and reproducible, reducing inherent reliance on judgment when toxicity information is limited. Our evaluation of membership in these fractions is highly consistent (˜80% on average across various fractions) with the empirical approach of MADEP and TPHCWG. Furthermore, the results support the general methodology of mixture risk assessment to assess both cancer and noncancer risk values after the application of fractionation.

Perspectives on Non-Animal Alternatives for Assessing Sensitization Potential in Allergic Contact Dermatitis

Skin sensitization remains a major environmental and occupational health hazard. Animal models have been used as the gold standard method of choice for estimating chemical sensitization potential. However, a growing international drive and consensus for minimizing animal usage have prompted the development of in vitro methods to assess chemical sensitivity. In this paper, we examine existing approaches including in silico models, cell and tissue based assays for distinguishing between sensitizers and irritants. The in silico approaches that have been discussed include Quantitative Structure Activity Relationships (QSAR) and QSAR based expert models that correlate chemical molecular structure with biological activity and mechanism based read-across models that incorporate compound electrophilicity. The cell and tissue based assays rely on an assortment of mono and co-culture cell systems in conjunction with 3D skin models. Given the complexity of allergen induced immune responses, and the limited ability of existing systems to capture the entire gamut of cellular and molecular events associated with these responses, we also introduce a microfabricated platform that can capture all the key steps involved in allergic contact sensitivity. Finally, we describe the development of an integrated testing strategy comprised of two or three tier systems for evaluating sensitization potential of chemicals.

Development, validation, and use of quantitative structure-activity relationship models of 5-hydroxytryptamine (2B) receptor ligands to identify novel receptor binders and putative valvulopathic compounds among common drugs

Some antipsychotic drugs are known to cause valvular heart disease by activating serotonin 5-HT(2B) receptors. We have developed and validated binary classification QSAR models capable of predicting potential 5-HT(2B) actives. The classification accuracies of the models built to discriminate 5-HT(2B) actives from the inactives were as high as 80% for the external test set. These models were used to screen in silico 59,000 compounds included in the World Drug Index, and 122 compounds were predicted as actives with high confidence. Ten of them were tested in radioligand binding assays and nine were found active, suggesting a success rate of 90%. All validated actives were then tested in functional assays, and one compound was identified as a true 5-HT(2B) agonist. We suggest that the QSAR models developed in this study could be used as reliable predictors to flag drug candidates that are likely to cause valvulopathy.

SAR/QSAR methods in public health practice

Methods of (Quantitative) Structure-Activity Relationship ((Q)SAR) modeling play an important and active role in ATSDR programs in support of the Agency mission to protect human populations from exposure to environmental contaminants. They are used for cross-chemical extrapolation to complement the traditional toxicological approach when chemical-specific information is unavailable. SAR and QSAR methods are used to investigate adverse health effects and exposure levels, bioavailability, and pharmacokinetic properties of hazardous chemical compounds. They are applied as a part of an integrated systematic approach in the development of Health Guidance Values (HGVs), such as ATSDR Minimal Risk Levels, which are used to protect populations exposed to toxic chemicals at hazardous waste sites. (Q)SAR analyses are incorporated into ATSDR documents (such as the toxicological profiles and chemical-specific health consultations) to support environmental health assessments, prioritization of environmental chemical hazards, and to improve study design, when filling the priority data needs (PDNs) as mandated by Congress, in instances when experimental information is insufficient. These cases are illustrated by several examples, which explain how ATSDR applies (Q)SAR methods in public health practice.

Assessing the toxic effects of ethylene glycol ethers using Quantitative Structure Toxicity Relationship models

Experimental determination of toxicity profiles consumes a great deal of time, money, and other resources. Consequently, businesses, societies, and regulators strive for reliable alternatives such as Quantitative Structure Toxicity Relationship (QSTR) models to fill gaps in toxicity profiles of compounds of concern to human health. The use of glycol ethers and their health effects have recently attracted the attention of international organizations such as the World Health Organization (WHO). The board members of Concise International Chemical Assessment Documents (CICAD) recently identified inadequate testing as well as gaps in toxicity profiles of ethylene glycol mono-n-alkyl ethers (EGEs). The CICAD board requested the ATSDR Computational Toxicology and Methods Development Laboratory to conduct QSTR assessments of certain specific toxicity endpoints for these chemicals. In order to evaluate the potential health effects of EGEs, CICAD proposed a critical QSTR analysis of the mutagenicity, carcinogenicity, and developmental effects of EGEs and other selected chemicals. We report here results of the application of QSTRs to assess rodent carcinogenicity, mutagenicity, and developmental toxicity of four EGEs: 2-methoxyethanol, 2-ethoxyethanol, 2-propoxyethanol, and 2-butoxyethanol and their metabolites. Neither mutagenicity nor carcinogenicity is indicated for the parent compounds, but these compounds are predicted to be developmental toxicants. The predicted toxicity effects were subjected to reverse QSTR (rQSTR) analysis to identify structural attributes that may be the main drivers of the developmental toxicity potential of these compounds.

Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling

The Carcinogenicity Reliability Database (CRDB) was constructed by collecting experimental carcinogenicity data on about 1,500 chemicals from six sources, including IARC, and NTP databases, and then by ranking their reliabilities into six unified categories. A wide variety of 911 organic chemicals were selected from the database for QSAR modeling, and 1,504 kinds of different molecular descriptors were calculated, based on their 3D molecular structures as modeled by the Dragon software. Positive (carcinogenic) and negative (non-carcinogenic) chemicals containing various substructures were counted using atom and functional group count descriptors, and the statistical significance of ratios of positives to negatives was tested for those substructures. Very few were judged to be strongly related to carcinogenicity, among substructures known to be responsible for carcinogens as revealed from biomedical studies. In order to develop QSAR models for the prediction of the carcinogenicities of a wide variety of chemicals with a satisfactory performance level, the relationship between the carcinogenicity data with improved reliability and a subset of significant descriptors selected from 1,504 Dragon descriptors was analyzed with a support vector machine (SVM) method: the classification function (SVC) for weighted data in LIBSVM program was used to classify chemicals into two carcinogenic categories (positive or negative), where weights were set depending on the reliabilities of the carcinogenicity data. The quality and stability of the models presented were tested by performing a dual cross-validation procedure. A single SVM model as the first step was developed for all the 911 chemicals using 250 selected descriptors, achieving an overall accuracy level, i.e., positive and negative correct estimate, of about 70%. In order to improve the accuracy of the final model, the 911 chemicals were classified into 20 mutually overlapping subgroups according to contained substructures, a specific SVM model was optimized for each subgroup, and the predicted carcinogenicities of the 911 chemicals were determined by the majorities of the outputs of the corresponding SVM models. The model developed on the basis of grouping of chemicals into 20 substructures predicts the carcinogenicities of a wide variety of chemicals with a satisfactory overall accuracy of approximately 80%.