An analysis of genetic toxicity, reproductive and developmental toxicity, and carcinogenicity data: II. Identification of genotoxicants, reprotoxicants, and carcinogens using in silico methods

Utility of in vitro assay data in read-across prediction of nongenotoxic carcinogenicity of pesticides for cytotoxicity-related rat tumors

Developing alternative methods for rat carcinogenicity studies remains challenging. This study aimed to establish a read-across method to predict nongenotoxic carcinogenicity in rats using molecular descriptors and in vitro assays. Based on 2-year rat carcinogenicity study results of agrochemicals, 80 compounds that caused benign or malignant tumors in the liver, thyroid, testis, uterus, ovary, breast, nasal cavity, stomach, or bladder/urethra and 46 compounds that did not were selected and subjected to cell-based cytotoxicity assays. Here, we focused on tumors associated with epithelial cell injury (nasal cavity, stomach, and bladder/urethra tumors). The read-across prediction was performed using neighboring substances, which were selected based on the Euclidean distance between the substances calculated using molecular descriptors. In some cases, neighboring substances were further selected based on the concordance of the in vitro assay results. The selection of neighboring substances based on the carcinogenicity-relevant descriptors and then on the cytotoxicity assay data improved the prediction accuracy (balanced accuracy: 0.752-0.821) compared to the accuracy with substances selected based on unselected descriptors alone (balanced accuracy: 0.294-0.582). These results suggest that the combined use of carcinogenicity-relevant descriptors and in vitro assays related to carcinogenic mechanisms is useful for read-across prediction of cytotoxicity-related tumors in rats.

Advancing the developmental and reproductive assessment in medical device preclinical safety evaluations

Currently, a standardized developmental and reproductive toxicity (DART) test of chemicals present in and released from medical devices does not exist. The objective of this study is to investigate if selected non-commercial and commercial in silico models, individually or in combination, can be used as a first step in addressing DART of medical device chemicals. A total of 478 polymeric material chemicals (268 DART and 210 non-DART chemicals) commonly used in medical devices were obtained from the European Chemicals Agency Database. Parameters, including sensitivity, specificity, and accuracy, were calculated and compared. Median sensitivity, specificity, and accuracy of the individual non-commercial or commercial in silico models were 0.529, 0.601, and 0.535, respectively. Median sensitivity, specificity, and accuracy of all possible combinations of two or three in silico models were 0.659, 0.467, and 0.573, respectively. Combining Leadscope and Percepta models resulted in the sensitivity of 0.524, specificity of 0.845, and best performing accuracy of 0.766. Integration of different in silico models may act as a useful first step for screening medical device-related chemicals with potential DART toxicity in addition to the effort in replacing, reducing, and refining in vivo DART testing in medical device preclinical safety assessments.

Review of the state of science and evaluation of currently available in silico prediction models for reproductive and developmental toxicity: A case study on pesticides

BACKGROUND

In silico methods for toxicity prediction have increased significantly in recent years due to the 3Rs principle. This also applies to predicting reproductive toxicology, which is one of the most critical factors in pesticide approval. The widely used quantitative structure-activity relationship (QSAR) models use experimental toxicity data to create a model that relates experimentally observed toxicity to molecular structures to predict toxicity. Aim of the study was to evaluate the available prediction models for developmental and reproductive toxicity regarding their strengths and weaknesses in a pesticide database.

METHODS

The reproductive toxicity of 315 pesticides, which have a GHS classification by ECHA, was compared with the prediction of different in silico models: VEGA, OECD (Q)SAR Toolbox, Leadscope Model Applier, and CASE Ultra by MultiCASE.

RESULTS

In all models, a large proportion (up to 77%) of all pesticides were outside the chemical space of the model. Analysis of the prediction of remaining pesticides revealed a balanced accuracy of the models between 0.48 and 0.66.

CONCLUSION

Overall, predictions were only meaningful in rare cases and therefore always require evaluation by an expert. The critical factors were the underlying data and determination of molecular similarity, which offer great potential for improvement.

In Silico Approaches In Carcinogenicity Hazard Assessment: Current Status and Future Needs

Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. In silico models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved in silico carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making. As part of an international effort to develop in silico toxicological protocols, a consortium of toxicologists, computational scientists, and regulatory scientists across several industries and governmental agencies evaluated the extent to which in silico models exist for each of the recently defined 10 key characteristics (KCs) of carcinogens. This position paper summarizes the current status of in silico tools for the assessment of each KC and identifies the data gaps that need to be addressed before a comprehensive in silico carcinogenicity protocol can be developed for regulatory use.

Stem Cells for Next Level Toxicity Testing in the 21st Century

The call for a paradigm change in toxicology from the United States National Research Council in 2007 initiates awareness for the invention and use of human-relevant alternative methods for toxicological hazard assessment. Simple 2D in vitro systems may serve as first screening tools, however, recent developments infer the need for more complex, multicellular organotypic models, which are superior in mimicking the complexity of human organs. In this review article most critical organs for toxicity assessment, i.e., skin, brain, thyroid system, lung, heart, liver, kidney, and intestine are discussed with regards to their functions in health and disease. Embracing the manifold modes-of-action how xenobiotic compounds can interfere with physiological organ functions and cause toxicity, the need for translation of such multifaceted organ features into the dish seems obvious. Currently used in vitro methods for toxicological applications and ongoing developments not yet arrived in toxicity testing are discussed, especially highlighting the potential of models based on embryonic stem cells and induced pluripotent stem cells of human origin. Finally, the application of innovative technologies like organs-on-a-chip and genome editing point toward a toxicological paradigm change moves into action.

Development of improved QSAR models for predicting the outcome of the in vivo micronucleus genetic toxicity assay

All drugs entering clinical trials are expected to undergo a series of in vitro and in vivo genotoxicity tests as outlined in the International Council on Harmonization (ICH) S2 (R1) guidance. Among the standard battery of genotoxicity tests used for pharmaceuticals, the in vivo micronucleus assay, which measures the frequency of micronucleated cells mostly from blood or bone marrow, is recommended for detecting clastogens and aneugens. (Quantitative) structure-activity relationship [(Q)SAR] models may be used as early screening tools by pharmaceutical companies to assess genetic toxicity risk during drug candidate selection. Models can also provide decision support information during regulatory review as part of the weight-of-evidence when experimental data are insufficient. In the present study, two commercial (Q)SAR platforms were used to construct in vivo micronucleus models from a recently enhanced in-house database of non-proprietary study findings in mice. Cross-validated performance statistics for the new models showed sensitivity of up to 74% and negative predictivity of up to 86%. In addition, the models demonstrated cross-validated specificity of up to 77% and coverage of up to 94%. These new models will provide more reliable predictions and offer an investigational approach for drug safety assessment with regards to identifying potentially genotoxic compounds.

Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses

The International Council on Harmonisation (ICH) M7(R1) guideline describes the use of complementary (quantitative) structure-activity relationship ((Q)SAR) models to assess the mutagenic potential of drug impurities in new and generic drugs. Historically, the CASE Ultra and Leadscope software platforms used two different statistical-based models to predict mutations at G-C (guanine-cytosine) and A-T (adenine-thymine) sites, to comprehensively assess bacterial mutagenesis. In the present study, composite bacterial mutagenicity models covering multiple mutation types were developed. These new models contain more than double the number of chemicals (n = 9,254 and n = 13,514) than the corresponding non-composite models and show better toxicophore coverage. Additionally, the use of a single composite bacterial mutagenicity model simplifies impurity analysis in an ICH M7 (Q)SAR workflow by reducing the number of model outputs requiring review. An external validation set of 388 drug impurities representing proprietary pharmaceutical chemical space showed performance statistics ranging from of 66-82% in sensitivity, 91-95% in negative predictivity and 96% in coverage. This effort represents a major enhancement to these (Q)SAR models and their use under ICH M7(R1), leading to improved patient safety through greater predictive accuracy, applicability, and efficiency when assessing the bacterial mutagenic potential of drug impurities.

Multilabel and Missing Label Methods for Binary Quantitative Structure-Activity Relationship Models: An Application for the Prediction of Adverse Drug Reactions

The prediction of adverse drug reactions in the discovery of new medicines is highly challenging. In the task of predicting the adverse reactions of chemical compounds, information about different targets is often available. Although we can focus on every adverse drug reaction prediction separately, multilabel approaches have been proven useful in many research areas for taking advantage of the relationship among the targets. However, when approaching the prediction problem from a multilabel point of view, we have to deal with the lack of information for some labels. This missing labels problem is a relevant issue in the field of cheminformatics approaches. This paper aims to predict the adverse drug reaction of commercial drugs using a multilabel approach where the possible presence of missing labels is also taken into consideration. We propose the use of multilabel methods to deal with the prediction of a large set of 27 different adverse reaction targets. We also propose the use of multilabel methods specifically designed to deal with the missing labels problem to test their ability to solve this difficult problem. The results show the validity of the proposed approach, demonstrating a superior performance of the multilabel method compared with the single-label approach in addressing the problem of adverse drug reaction prediction.

In silico prediction of chromosome damage: comparison of three (Q)SAR models

Two major endpoints for genotoxicity tests are gene mutation and chromosome damage (CD), which includes clastogenicity and aneugenicity detected by chromosomal aberration (CA) test or micronucleus (MN) test. Many in silico prediction systems for bacterial mutagenicity (i.e. Ames test results) have been developed and marketed. They show good performance for prediction of Ames mutagenicity. On the other hand, it seems that in silico prediction of CD does not progress as much as Ames prediction. Reasons for this include different mechanisms and detection methods, many false positives and conflicting test results. However, some (quantitative) structure-activity relationship ((Q)SAR) models (e.g. Derek Nexus [Derek], ADMEWorks [AWorks] and CASE Ultra [MCase]) can predict CA test results. Therefore, performances of the three (Q)SAR models were compared using the expanded Carcinogenicity Genotoxicity eXperience (CGX) dataset for understanding current situations and future development. The constructed dataset contained 440 chemicals (325 carcinogens and 115 non-carcinogens). Sensitivity, specificity, accuracy or applicability of each model were 56.0, 86.9, 68.6 or 89.1% in Derek, 67.7, 61.5, 65.2 or 99.3% in AWorks, and 91.0, 64.9, 80.5 or 97.7% in MCase, respectively. The performances (sensitivity and accuracy) of MCase were higher than those of Derek or AWorks. Analysis of predictivity of (Q)SAR models of certain chemical classes revealed no remarkable differences among the models. The tendency of positive prediction by (Q)SAR models was observed in alkylating agents, aromatic amines or amides, aromatic nitro compounds, epoxides, halides and N-nitro or N-nitroso compounds. In an additional investigation, high sensitivity but low specificity was noted in in vivo MN prediction by MCase. Refinement of test data to be used for in silico system (e.g. consideration of cytotoxicity or re-evaluation of conflicting test results) will be needed to improve performance of CD prediction.

Construction and application of (Q)SAR models to predict chemical-induced in vitro chromosome aberrations

In drug development, genetic toxicology studies are conducted using in vitro and in vivo assays to identify potential mutagenic and clastogenic effects, as outlined in the International Council for Harmonisation (ICH) S2 regulatory guideline. (Quantitative) structure-activity relationship ((Q)SAR) models that predict assay outcomes can be used as an early screen to prioritize pharmaceutical candidates, or later during product development to evaluate safety when experimental data are unavailable or inconclusive. In the current study, two commercial QSAR platforms were used to build models for in vitro chromosomal aberrations in Chinese hamster lung (CHL) and Chinese hamster ovary (CHO) cells. Cross-validated CHL model predictive performance showed sensitivity of 80 and 82%, and negative predictivity of 75 and 76% based on 875 training set compounds. For CHO, sensitivity of 61 and 67% and negative predictivity of 68 and 74% was achieved based on 817 training set compounds. The predictive performance of structural alerts in a commercial expert rule-based SAR software was also investigated and showed positive predictivity of 48-100% for selected alerts. Case studies examining incorrectly-predicted compounds, non-DNA-reactive clastogens, and recently-approved pharmaceuticals are presented, exploring how an investigational approach using similarity searching and expert knowledge can improve upon individual (Q)SAR predictions of the clastogenicity of drugs.

In silico toxicology: comprehensive benchmarking of multi-label classification methods applied to chemical toxicity data

One goal of toxicity testing, among others, is identifying harmful effects of chemicals. Given the high demand for toxicity tests, it is necessary to conduct these tests for multiple toxicity endpoints for the same compound. Current computational toxicology methods aim at developing models mainly to predict a single toxicity endpoint. When chemicals cause several toxicity effects, one model is generated to predict toxicity for each endpoint, which can be labor and computationally intensive when the number of toxicity endpoints is large. Additionally, this approach does not take into consideration possible correlation between the endpoints. Therefore, there has been a recent shift in computational toxicity studies toward generating predictive models able to predict several toxicity endpoints by utilizing correlations between these endpoints. Applying such correlations jointly with compounds' features may improve model's performance and reduce the number of required models. This can be achieved through multi‐label classification methods. These methods have not undergone comprehensive benchmarking in the domain of predictive toxicology. Therefore, we performed extensive benchmarking and analysis of over 19,000 multi‐label classification models generated using combinations of the state‐of‐the‐art methods. The methods have been evaluated from different perspectives using various metrics to assess their effectiveness. We were able to illustrate variability in the performance of the methods under several conditions. This review will help researchers to select the most suitable method for the problem at hand and provide a baseline for evaluating new approaches. Based on this analysis, we provided recommendations for potential future directions in this area.

This article is categorized under: 1

Computer and Information Science > Chemoinformatics

2

Computer and Information Science > Computer Algorithms and Programming

Carcinogenic potential of sanguinarine, a phytochemical used in 'therapeutic' black salve and mouthwash

Black salves are escharotic skin cancer therapies in clinical use since the mid 19th century. Sanguinaria canadensis, a major ingredient of black salve formulations, contains a number of bioactive phytochemicals including the alkaloid sanguinarine. Despite its prolonged history of clinical use, conflicting experimental results have prevented the carcinogenic potential of sanguinarine from being definitively determined. Sanguinarine has a molecular structure similar to known polyaromatic hydrocarbon carcinogens and is a DNA intercalator. Sanguinarine also generates oxidative and endoplasmic reticulum stress resulting in the unfolded protein response and the formation of 8-hydroxyguanine genetic lesions. Sanguinarine has been the subject of contradictory in vitro and in vivo genotoxicity and murine carcinogenesis test results that have delayed its carcinogenic classification. Despite this, epidemiological studies have linked mouthwash that contains sanguinarine with the development of oral leukoplakia. Sanguinarine is also proposed as an aetiological agent in gallbladder carcinoma. This literature review investigates the carcinogenic potential of sanguinarine. Reasons for contradictory genotoxicity and carcinogenesis results are explored, knowledge gaps identified and a strategy for determining the carcinogenic potential of sanguinarine especialy relating to black salve are discussed. As patients continue to apply black salve, especially to skin regions suffering from field cancerization and skin malignancies, an understanding of the genotoxic and carcinogenic potential of sanguinarine is of urgent clinical relevance.

MPD3: a useful medicinal plants database for drug designing

Medicinal plants are the main natural pools for the discovery and development of new drugs. In the modern era of computer-aided drug designing (CADD), there is need of prompt efforts to design and construct useful database management system that allows proper data storage, retrieval and management with user-friendly interface. An inclusive database having information about classification, activity and ready-to-dock library of medicinal plant's phytochemicals is therefore required to assist the researchers in the field of CADD. The present work was designed to merge activities of phytochemicals from medicinal plants, their targets and literature references into a single comprehensive database named as Medicinal Plants Database for Drug Designing (MPD3). The newly designed online and downloadable MPD3 contains information about more than 5000 phytochemicals from around 1000 medicinal plants with 80 different activities, more than 900 literature references and 200 plus targets. The designed database is deemed to be very useful for the researchers who are engaged in medicinal plants research, CADD and drug discovery/development with ease of operation and increased efficiency. The designed MPD3 is a comprehensive database which provides most of the information related to the medicinal plants at a single platform. MPD3 is freely available at: http://bioinform.info .

In silico toxicology: computational methods for the prediction of chemical toxicity

Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late-stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models. WIREs Comput Mol Sci 2016, 6:147-172. doi: 10.1002/wcms.1240 For further resources related to this article, please visit the WIREs website.

The Consultancy Activity on In Silico Models for Genotoxic Prediction of Pharmaceutical Impurities

The toxicological assessment of DNA-reactive/mutagenic or clastogenic impurities plays an important role in the regulatory process for pharmaceuticals; in this context, in silico structure-based approaches are applied as primary tools for the evaluation of the mutagenic potential of the drug impurities. The general recommendations regarding such use of in silico methods are provided in the recent ICH M7 guideline stating that computational (in silico) toxicology assessment should be performed using two (Q)SAR prediction methodologies complementing each other: a statistical-based method and an expert rule-based method.Based on our consultant experience, we describe here a framework for in silico assessment of mutagenic potential of drug impurities. Two main applications of in silico methods are presented: (1) support and optimization of drug synthesis processes by providing early indication of potential genotoxic impurities and (2) regulatory evaluation of genotoxic potential of impurities in compliance with the ICH M7 guideline. Some critical case studies are also discussed.

The biomechanisms of metal and metal-oxide nanoparticles' interactions with cells

Humans are increasingly exposed to nanoparticles (NPs) in medicine and in industrial settings, where significant concentrations of NPs are common. However, NP interactions with and effects on biomolecules and organisms have only recently been addressed. Within we review the literature regarding proposed modes of action for metal and metal-oxide NPs, two of the most prevalent types manufactured. Iron-oxide NPs, for instance, are used as tracers for magnetic resonance imaging of oncological tumors and as vehicles for therapeutic drug delivery. Factors and theories that determine the physicochemical and biokinetic behaviors of NPs are discussed, along with the observed toxicological effects of NPs on cells. Key thermodynamic and kinetic models that explain the sources of energy transfer from NPs to biological targets are summarized, in addition to quantitative structural activity relationship (QSAR) modeling efforts. Future challenges for nanotoxicological research are discussed. We conclude that NP studies based on cell culture are often inconsistent and underestimate the toxicity of NPs. Thus, the effect of NPs needs to be examined in whole animal systems.

Developmental toxicity prediction

Developmental toxicity may be estimated using commercial and noncommercial software that is already available in the market and/or literature, or models may be built from scratch using both commercial and noncommercial software packages. In this chapter, commonly available software programs that can predict the developmental toxicity of chemicals are described. In addition, a method for developing qualitative structure-activity relationship (SAR) models to predict the developmental toxicity of chemicals qualitatively (yes/no prediction) and quantitative structure-activity relationship (QSAR) models to predict quantitative estimates (e.g., LOAEL) of developmental toxicants is also described in this chapter. Additional information described in this chapter include methods to predict physicochemical properties of chemicals that can be used as descriptor variables in the model building process, statistical methods that be used to build QSAR models as well as methods to validate the models that are developed. Most of the methods described in this chapter can be used to develop models for health endpoints other than developmental toxicity as well.

Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential

The potential (eco)toxicological hazard posed by engineered nanoparticles is a major scientific and societal concern since several industrial sectors (e.g. electronics, biomedicine, and cosmetics) are exploiting the innovative properties of nanostructures resulting in their large-scale production. Many consumer products contain nanomaterials and, given their complex life-cycle, it is essential to anticipate their (eco)toxicological properties in a fast and inexpensive way in order to mitigate adverse effects on human health and the environment. In this context, the application of the structure-toxicity paradigm to nanomaterials represents a promising approach. Indeed, according to this paradigm, it is possible to predict toxicological effects induced by chemicals on the basis of their structural similarity with chemicals for which toxicological endpoints have been previously measured. These structure-toxicity relationships can be quantitative or qualitative in nature and they can predict toxicological effects directly from the physicochemical properties of the entities (e.g. nanoparticles) of interest. Therefore, this approach can aid in prioritizing resources in toxicological investigations while reducing the ethical and monetary costs that are related to animal testing. The purpose of this review is to provide a summary of recent key advances in the field of QSAR modelling of nanomaterial toxicity, to identify the major gaps in research required to accelerate the use of quantitative structure-activity relationship (QSAR) methods, and to provide a roadmap for future research needed to achieve QSAR models useful for regulatory purposes.

Genetic toxicology in the 21st century: reflections and future directions

A symposium at the 40th anniversary of the Environmental Mutagen Society, held from October 24-28, 2009 in St. Louis, MO, surveyed the current status and future directions of genetic toxicology. This article summarizes the presentations and provides a perspective on the future. An abbreviated history is presented, highlighting the current standard battery of genotoxicity assays and persistent challenges. Application of computational toxicology to safety testing within a regulatory setting is discussed as a means for reducing the need for animal testing and human clinical trials, and current approaches and applications of in silico genotoxicity screening approaches across the pharmaceutical industry were surveyed and are reported here. The expanded use of toxicogenomics to illuminate mechanisms and bridge genotoxicity and carcinogenicity, and new public efforts to use high-throughput screening technologies to address lack of toxicity evaluation for the backlog of thousands of industrial chemicals in the environment are detailed. The Tox21 project involves coordinated efforts of four U.S. Government regulatory/research entities to use new and innovative assays to characterize key steps in toxicity pathways, including genotoxic and nongenotoxic mechanisms for carcinogenesis. Progress to date, highlighting preliminary test results from the National Toxicology Program is summarized. Finally, an overview is presented of ToxCast™, a related research program of the U.S. Environmental Protection Agency, using a broad array of high throughput and high content technologies for toxicity profiling of environmental chemicals, and computational toxicology modeling. Progress and challenges, including the pressing need to incorporate metabolic activation capability, are summarized.

New and emerging technologies for genetic toxicity testing

The International Life Sciences Institute (ILSI) Health and Environmental Sciences Institute (HESI) Project Committee on the Relevance and Follow-up of Positive Results in In Vitro Genetic Toxicity (IVGT) Testing established an Emerging Technologies and New Strategies Workgroup to review the current State of the Art in genetic toxicology testing. The aim of the workgroup was to identify promising technologies that will improve genotoxicity testing and assessment of in vivo hazard and risk, and that have the potential to help meet the objectives of the IVGT. As part of this initiative, HESI convened a workshop in Washington, DC in May 2008 to discuss mature, maturing, and emerging technologies in genetic toxicology. This article collates the abstracts of the New and Emerging Technologies Workshop together with some additional technologies subsequently considered by the workgroup. Each abstract (available in the online version of the article) includes a section addressed specifically to the strengths, weaknesses, opportunities, and threats associated with the respective technology. Importantly, an overview of the technologies and an indication of how their use might be aligned with the objectives of IVGT are presented. In particular, consideration was given with regard to follow-up testing of positive results in the standard IVGT tests (i.e., Salmonella Ames test, chromosome aberration assay, and mouse lymphoma assay) to add weight of evidence and/or provide mechanism of action for improved genetic toxicity risk assessments in humans.

An integrated QSAR-PBPK modelling approach for predicting the inhalation toxicokinetics of mixtures of volatile organic chemicals in the rat

The objective of this study was to predict the inhalation toxicokinetics of chemicals in mixtures using an integrated QSAR-PBPK modelling approach. The approach involved: (1) the determination of partition coefficients as well as V(max) and K(m) based solely on chemical structure for 53 volatile organic compounds, according to the group contribution approach; and (2) using the QSAR-driven coefficients as input in interaction-based PBPK models in the rat to predict the pharmacokinetics of chemicals in mixtures of up to 10 components (benzene, toluene, m-xylene, o-xylene, p-xylene, ethylbenzene, dichloromethane, trichloroethylene, tetrachloroethylene, and styrene). QSAR-estimated values of V(max) varied compared with experimental results by a factor of three for 43 out of 53 studied volatile organic compounds (VOCs). K(m) values were within a factor of three compared with experimental values for 43 out of 53 VOCs. Cross-validation performed as a ratio of predicted residual sum of squares and sum of squares of the response value indicates a value of 0.108 for V(max) and 0.208 for K(m). The integration of QSARs for partition coefficients, V(max) and K(m), as well as setting the K(m) equal to K(i) (metabolic inhibition constant) within the mixture PBPK model allowed to generate simulations of the inhalation pharmacokinetics of benzene, toluene, m-xylene, o-xylene, p-xylene, ethylbenzene, dichloromethane, trichloroethylene, tetrachloroethylene and styrene in various mixtures. Overall, the present study indicates the potential usefulness of the QSAR-PBPK modelling approach to provide first-cut evaluations of the kinetics of chemicals in mixtures of increasing complexity, on the basis of chemical structure.

A Data-Based Assessment of Alternative Strategies for Identification of Potential Human Cancer Hazards

The two-year cancer bioassay in rodents remains the primary testing strategy for in-life screening of compounds that might pose a potential cancer hazard. Yet experimental evidence shows that cancer is often secondary to a biological precursor effect, the mode of action is sometimes not relevant to humans, and key events leading to cancer in rodents from nongenotoxic agents usually occur well before tumorigenesis and at the same or lower doses than those producing tumors. The International Life Sciences Institute (ILSI) Health and Environmental Sciences Institute (HESI) hypothesized that the signals of importance for human cancer hazard identification can be detected in shorter-term studies. Using the National Toxicology Program (NTP) database, a retrospective analysis was conducted on sixteen chemicals with liver, lung, or kidney tumors in two-year rodent cancer bioassays, and for which short-term data were also available. For nongenotoxic compounds, results showed that cellular changes indicative of a tumorigenic endpoint can be identified for many, but not all, of the chemicals producing tumors in two-year studies after thirteen weeks utilizing conventional endpoints. Additional endpoints are needed to identify some signals not detected with routine evaluation. This effort defined critical questions that should be explored to improve the predictivity of human carcinogenic risk.

A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents

BACKGROUND

Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public-private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening.

OBJECTIVE

A wealth of available biological data requires new computational approaches to link chemical structure, in vitro data, and potential adverse health effects.

METHODS AND RESULTS

A database containing experimental cytotoxicity values for in vitro half-maximal inhibitory concentration (IC(50)) and in vivo rodent median lethal dose (LD(50)) for more than 300 chemicals was compiled by Zentralstelle zur Erfassung und Bewertung von Ersatz- und Ergaenzungsmethoden zum Tierversuch (ZEBET; National Center for Documentation and Evaluation of Alternative Methods to Animal Experiments). The application of conventional quantitative structure-activity relationship (QSAR) modeling approaches to predict mouse or rat acute LD(50) values from chemical descriptors of ZEBET compounds yielded no statistically significant models. The analysis of these data showed no significant correlation between IC(50) and LD(50). However, a linear IC(50) versus LD(50) correlation could be established for a fraction of compounds. To capitalize on this observation, we developed a novel two-step modeling approach as follows. First, all chemicals are partitioned into two groups based on the relationship between IC(50) and LD(50) values: One group comprises compounds with linear IC(50) versus LD(50) relationships, and another group comprises the remaining compounds. Second, we built conventional binary classification QSAR models to predict the group affiliation based on chemical descriptors only. Third, we developed k-nearest neighbor continuous QSAR models for each subclass to predict LD(50) values from chemical descriptors. All models were extensively validated using special protocols.

CONCLUSIONS

The novelty of this modeling approach is that it uses the relationships between in vivo and in vitro data only to inform the initial construction of the hierarchical two-step QSAR models. Models resulting from this approach employ chemical descriptors only for external prediction of acute rodent toxicity.

Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans: Part C: use of QSAR and an expert system for the estimation of the mechanism of action of drug-induced hepatobiliary and urinary tract toxicities

This report describes an in silico methodology to predict off-target pharmacologic activities and plausible mechanisms of action (MOAs) associated with serious and unexpected hepatobiliary and urinary tract adverse effects in human patients. The investigation used a database of 8,316,673 adverse event (AE) reports observed after drugs had been marketed and AEs noted in the published literature that were linked to 2124 chemical structures and 1851 approved clinical indications. The Integrity database of drug patent and literature studies was used to find pharmacologic targets and proposed clinical indications. BioEpisteme QSAR software was used to predict possible molecular targets of drug molecules and Derek for Windows expert system software to predict chemical structural alerts and plausible MOAs for the AEs. AEs were clustered into five types of liver injury: liver enzyme disorders, cytotoxic injury, cholestasis and jaundice, bile duct disorders, and gall bladder disorders, and six types of urinary tract injury: acute renal disorders, nephropathies, bladder disorders, kidney function tests, blood in urine, and urolithiasis. Results showed that drug-related AEs were highly correlated with: (1) known drug class warnings, (2) predicted off-target activities of the drugs, and (3) a specific subset of clinical indications for which the drug may or may not have been prescribed.

An update on the genotoxicity and carcinogenicity of marketed pharmaceuticals with reference to in silico predictivity

Information from the 1999 through 2008 Physicians' Desk Reference (PDR) was used to evaluate the genotoxicity of marketed drugs. Where available, data regarding the rodent carcinogenicity results were included (PDR and Gold potency database). In addition, computational predictivity of genotoxicity (DEREK, MC4PC) is included and expanded upon from two previous reviews. The present paper contains genotoxicity data on 545 marketed drugs. Excluded from analysis were most cytotoxic anti-cancer and antiviral drugs, nucleosides (all with known mechanistic genotoxicity), steroids with class-specific genotoxicity and biologicals or peptide-based drugs. Per assay type, the percentage of positive drugs was: Bacterial mutagenesis assay: 38/525 (7.1%), in vitro chromosome aberrations: 88/380 (26.1%); mouse lymphoma assays (MLA): 32/163 (19.1%), in vivo cytogenetics: 49/438 (11.1%). The relationship among positive genetic toxicity findings, rodent carcinogenicity, and in silico prediction is discussed. Finally, supporting evidence is presented for the idea that the presence of an N-dialkyl group or piperidine aryl ketone may somehow be associated with genotoxicity, perhaps through DNA intercalation and consequent DNA topoisomerase II inhibition.

Genotoxicity of water from the Songhua River, China, in 1994-1995 and 2002-2003: Potential risks for human health

A previous study showed that the cancer mortalities are higher for residents who lived nearby the Songhua River heavily polluted by organic contamination. It is important to determine its risk of carcinogenic potential. Short-term genotoxic bio-assays using Salmonella, Sister Chromatid Exchange (SCE), and Micronuclei (MN) assays were employed to examine the genotoxic activity of ether extracts of water samples taken from the Songhua River. The results of the Salmonella bioassay indicated that there were indirect frame-shift mutagens in the water samples. A dose-response relationship for the SCE and MN assays was obtained. These results showed that organic extracts of water samples have genotoxic activity and the risk of carcinogenic potential to human health. The mutagenesis of water samples had changed compared to the results in 1994-1995. An increasing trend of risk of carcinogenic potential in the Songhua River after ten years should be noted and needs to be studied further.

Prediction of chemical carcinogenicity by machine learning approaches

In this paper we report a successful application of machine learning approaches to the prediction of chemical carcinogenicity. Two different approaches, namely a support vector machine (SVM) and artificial neural network (ANN), were evaluated for predicting chemical carcinogenicity from molecular structure descriptors. A diverse set of 844 compounds, including 600 carcinogenic (CG+) and 244 noncarcinogenic (CG-) molecules, was used to estimate the accuracies of these approaches. The database was divided into two sets: the model construction set and the independent test set. Relevant molecular descriptors were selected by a hybrid feature selection method combining Fischer's score and Monte Carlo simulated annealing from a wide set of molecular descriptors, including physiochemical properties, constitutional, topological, and geometrical descriptors. The first model validation method was based a five-fold cross-validation method, in which the model construction set is split into five subsets. The five-fold cross-validation was used to select descriptors and optimise the model parameters by maximising the averaged overall accuracy. The final SVM model gave an averaged prediction accuracy of 90.7% for CG+ compounds, 81.6% for CG- compounds and 88.1% for the overall accuracy, while the corresponding ANN model provided an averaged prediction accuracy of 86.1% for CG+ compounds, 79.3% for CG- compounds and 84.2% for the overall accuracy. These results indicate that the hybrid feature selection method is very efficient and the selected descriptors are truly relevant to the carcinogenicity of compounds. Another model validation method, i.e. a hold-out method, was used to build the classification model using the selected descriptors and the optimised model parameters, in which the whole model construction set was used to build the classification model and the independent test set was used to test the predictive ability of the model. The SVM model gave a prediction accuracy of 87.6% for CG+ compounds, 79.1% for CG- compounds and 85.0% for the overall accuracy. The ANN model gave a prediction accuracy of 85.6% for CG+ compounds, 79.1% for CG- compounds and 83.6% for the overall accuracy. The results indicate that the built models are potentially useful for facilitating the prediction of chemical carcinogenicity of untested compounds.