A review of the toxicity of ingredients in e-cigarettes, including those ingredients having the FDA's "Generally Recognized as Safe (GRAS)" regulatory status for use in food.

INTRODUCTION: Some firms and marketers of electronic cigarettes (e-cigarettes; a type of electronic nicotine delivery system (ENDS)) and refill liquids (e-liquids) have made claims about the safety of ingredients used in their products based on the term "GRAS or Generally Recognized As Safe" (GRAS). However, GRAS is a provision within the definition of a food additive under section 201(s) (21 U.S.C. 321(s)) of the U.S. Federal Food Drug and Cosmetic Act (FD&C Act). Food additives and GRAS substances are by the FD&C Act definition intended for use in food, thus safety is based on oral consumption; the term GRAS cannot serve as an indicator of the toxicity of e-cigarette ingredients when aerosolized and inhaled (i.e., vaped). There is no legal or scientific support for labeling e-cigarette product ingredients as "GRAS". This review discusses our concerns with the GRAS provision being applied to e-cigarette products and provides examples of chemical compounds that have been used as food ingredients but have been shown to lead to adverse health effects when inhaled. The review provides scientific insight into the toxicological evaluation of e-liquid ingredients and their aerosols to help determine the potential respiratory risks associated with their use in e-cigarettes. IMPLICATIONS: The rise in prevalence of e-cigarette use and emerging evidence of adverse effects, particularly on lung health, warrant assessing all aspects of e-cigarette toxicity. One development is manufacturers' stated or implied claims of the safety of using e-cigarette products containing ingredients determined to be "Generally Recognized As Safe" (GRAS) for use in food. Such claims, typically placed on e-cigarette product labels and used in marketing, are unfounded, as pointed out by the United States Food and Drug Administration (FDA)1 and the Flavor and Extract Manufacturers Association (FEMA)2. Assessment of inhalation health risks of all ingredients used in e-liquids, including those claimed to be GRAS, is warranted.

Exploring the potential neurotoxicity of vaping vitamin E or vitamin E acetate.

Serious adverse health effects have been reported with the use of vaping products, including neurologic disorders and e-cigarette or vaping product use-associated lung injury (EVALI). Vitamin E acetate, likely added as a diluent to cannabis-containing products, was linked to EVALI. Literature searches were performed on vitamin E and vitamin E acetate-associated neurotoxicity. Blood brain barrier (BBB) penetration potential of vitamin E and vitamin E acetate were evaluated using cheminformatic techniques. Review of the literature showed that the neurotoxic potential of inhalation exposures to these compounds in humans is unknown. Physico-chemical properties demonstrate these compounds are lipophilic, and molecular weights indicate vitamin E and vitamin E acetate have the potential for BBB permeability. Computational models also predict both compounds may cross the BBB via passive diffusion. Based on literature search, no experimental nonclinical studies and clinical information on the neurotoxic potential of vitamin E via inhalation. Neurotoxic effects from pyrolysis by-product, phenyl acetate, structurally analogous to vitamin E acetate, suggests vitamin E acetate has potential for central nervous system (CNS) impairment. Cheminformatic model predictions provide a theoretical basis for potential CNS permeability of these inhaled dietary ingredients suggesting prioritization to evaluate for potential hazard to the CNS.

A Computational Approach for Respiratory Hazard Identification of Flavor Chemicals in Tobacco Products.

Flavor chemicals contribute to the appeal and toxicity of tobacco products, including electronic nicotine delivery systems (ENDS). The assortment of flavor chemicals available for use in tobacco products is extensive. In this study, a chemistry-driven computational approach was used to evaluate flavor chemicals based on intrinsic hazardous structures and reactivity of chemicals. A large library of 3012 unique flavor chemicals was compiled from publicly available information. Next, information was computed and collated based on their (1) physicochemical properties, (2) global harmonization system (GHS) health hazard classification, (3) structural alerts linked to the chemical's reactivity, instability, or toxicity, and (4) common substructure shared with FDA's harmful and potentially harmful constituents (HPHCs) flavor chemicals that are respiratory toxicants. Computational analysis of the constructed flavor library flagged 638 chemicals with GHS classified respiratory health hazards, 1079 chemicals with at least one structural alert, and 2297 chemicals with substructural similarity to FDA's established and proposed list of HPHCs. A subsequent analysis was performed on a subset of 173 chemicals in the flavor library that are respiratory health hazards, contain structural alerts as well as flavor HPHC substructures. Four general toxicophore structures with an increased potential for respiratory toxicity were then identified. In summary, computational methods are efficient tools for hazard identification and understanding structure-toxicity relationship. With appropriate context of use and interpretation, in silico methods may provide scientific evidence to support toxicological evaluations of chemicals in or emitted from tobacco products.

Investigating DNA adduct formation by flavor chemicals and tobacco byproducts in electronic nicotine delivery system (ENDS) using in silico approaches.

The presence of flavors is one of the commonly cited reasons for use of e-cigarettes by youth; however, the potential harms from inhaling these chemicals and byproducts have not been extensively studied. One mechanism of interest is DNA adduct formation, which may lead to carcinogenesis. We identified two chemical classes of flavors found in tobacco products and byproducts, alkenylbenzenes and aldehydes, documented to form DNA adducts. Using in silico toxicology approaches, we identified structural analogs to these chemicals without DNA adduct information. We conducted a structural similarity analysis and also generated in silico model predictions of these chemicals for genotoxicity, mutagenicity, carcinogenicity, and skin sensitization. The empirical and in silico data were compared, and we identified strengths and limitations of these models. Good concordance (80-100%) was observed between DNA adduct formation and models predicting mammalian mutagenicity (mouse lymphoma sassy L5178Y) and skin sensitization for both chemical classes. On the other hand, different prediction profiles were observed for the two chemical classes for the modeled endpoints, unscheduled DNA synthesis and bacterial mutagenicity. These results are likely due to the different mode of action between the two chemical classes, as aldehydes are direct acting agents, while alkenylbenzenes require bioactivation to form electrophilic intermediates, which form DNA adducts. The results of this study suggest that an in silico prediction for the mouse lymphoma assay L5178Y, may serve as a surrogate endpoint to help predict DNA adduct formation for chemicals found in tobacco products such as flavors and byproducts.

Development of a Nicotinic Acetylcholine Receptor nAChR α7 Binding Activity Prediction Model.

Despite the well-known adverse health effects associated with tobacco use, addiction to nicotine found in tobacco products causes difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the physiological targets of nicotine and facilitate addiction to tobacco products. The nAChR-α7 subtype plays an important role in addiction; therefore, predicting the binding activity of tobacco constituents to nAChR-α7 is an important component for assessing addictive potential of tobacco constituents. We developed an α7 binding activity prediction model based on a large training data set of 843 chemicals with human α7 binding activity data extracted from PubChem and ChEMBL. The model was tested using 1215 chemicals with rat α7 binding activity data from the same databases. Based on the competitive docking results, the docking scores were partitioned to the key residues that play important roles in the receptor-ligand binding. A decision forest was used to train the human α7 binding activity prediction model based on the partition of docking scores. Five-fold cross validations were conducted to estimate the performance of the decision forest models. The developed model was used to predict the potential human α7 binding activity for 5275 tobacco constituents. The human α7 binding activity data for 84 of the 5275 tobacco constituents were experimentally measured to confirm and empirically validate the prediction results. The prediction accuracy, sensitivity, and specificity were 64.3, 40.0, and 81.6%, respectively. The developed prediction model of human α7 may be a useful tool for high-throughput screening of potential addictive tobacco constituents.

In vitro and in silico genetic toxicity screening of flavor compounds and other ingredients in tobacco products with emphasis on ENDS.

Electronic nicotine delivery systems (ENDS) are regulated tobacco products and often contain flavor compounds. Given the concern of increased use and the appeal of ENDS by young people, evaluating the potential of flavors to induce DNA damage is important for health hazard identification. In this study, alternative methods were used as prioritization tools to study the genotoxic mode of action (MoA) of 150 flavor compounds. In particular, clastogen-sensitive (γH2AX and p53) and aneugen-sensitive (p-H3 and polyploidy) biomarkers of DNA damage in human TK6 cells were aggregated through a supervised three-pronged ensemble machine learning prediction model to prioritize chemicals based on genotoxicity. In addition, in silico quantitative structure-activity relationship (QSAR) models were used to predict genotoxicity and carcinogenic potential. The in vitro assay identified 25 flavors as positive for genotoxicity: 15 clastogenic, eight aneugenic and two with a mixed MoA (clastogenic and aneugenic). Twenty-three of these 25 flavors predicted to induce DNA damage in vitro are documented in public literature to be in e-liquid or in the aerosols produced by ENDS products with youth-appealing flavors and names. QSAR models predicted 46 (31%) of 150 compounds having at least one positive call for mutagenicity, clastogenicity or rodent carcinogenicity, 49 (33%) compounds were predicted negative for all three endpoints, and remaining compounds had no prediction call. The parallel use of these predictive technologies to elucidate MoAs for potential genetic damage, hold utility as a screening strategy. This study is the first high-content and high-throughput genotoxicity screening study with an emphasis on flavors in ENDS products.

Predicting the mutagenic potential of chemicals in tobacco products using in silico toxicology tools.

Tobacco products contain thousands of chemicals, including addictive and toxic chemicals. We utilized in silico toxicology tools to predict in a validation test and in a separate screening test, the mutagenic potential of chemicals reported in tobacco products and tobacco smoke. Different publicly available (quantitative) structure-activity relationship (Q)SAR software platforms were used in this study. The models were validated against 900 chemicals relevant to tobacco for which experimental Ames mutagenicity data are available from public sources. The predictive performance of the individual and combined (Q)SAR models was evaluated using various performance metrics. All the (Q)SAR models represented >95% of the tobacco chemical space indicating a high potential for screening tobacco products. All the models performed well and predicted mutagens and nonmutagens with 75-95% accuracy, 66-94% sensitivity and 73-97% specificity. Subsequently, in a screening test, a combination of complementary SAR-based and QSAR-based models was used to predict the mutagenicity of 6820 chemicals catalogued in tobacco products and/or tobacco smoke. More than 1200 chemicals identified in tobacco products are predicted to have mutagenic potential, with 900 potential mutagens in tobacco smoke. This research demonstrates the validity of in silico (Q)SAR tools to make mutagenicity predictions for chemicals in tobacco products and/or tobacco smoke, and suggest they hold utility as screening tools for hazard identification to inform tobacco regulatory science.

Investigative analysis of blood-brain barrier penetrating potential of electronic nicotine delivery systems (e-cigarettes) chemicals using predictive computational models.

INTRODUCTION: Seizures are known potential side effects of nicotine toxicity and have been reported in electronic nicotine delivery systems (ENDS, e-cigarettes) users, with the majority involving youth or young adults. AREAS COVERED: Using chemoinformatic computational models, chemicals (including flavors) documented to be present in ENDS were compared to known neuroactive compounds to predict the blood-brain barrier (BBB) penetration potential, central nervous system (CNS) activity, and their structural similarities. The literature search used PubMed/Google Scholar, through September 2023, to identify individual chemicals in ENDS and neuroactive compounds.The results show that ENDS chemicals in this study contain >60% structural similarity to neuroactive compounds based on chemical fingerprint similarity analyses. The majority of ENDS chemicals we studied were predicted to cross the BBB, with approximately 60% confidence, and were also predicted to have CNS activity; those not predicted to passively diffuse through the BBB may be actively transported through the BBB to elicit CNS impacts, although it is currently unknown. EXPERT OPINION: In lieu of in vitro and in vivo testing, this study screens ENDS chemicals for potential CNS activity and predicts BBB penetration potential using computer-based models, allowing for prioritization for further study and potential early identification of CNS toxicity.

A novel QSAR model of Salmonella mutagenicity and its application in the safety assessment of drug impurities.

As indicated in ICH M7 draft guidance, in silico predictive tools including statistically-based QSARs and expert analysis may be used as a computational assessment for bacterial mutagenicity for the qualification of impurities in pharmaceuticals. To address this need, we developed and validated a QSAR model to predict Salmonella t. mutagenicity (Ames assay outcome) of pharmaceutical impurities using Prous Institute's Symmetry(SM), a new in silico solution for drug discovery and toxicity screening, and the Mold2 molecular descriptor package (FDA/NCTR). Data was sourced from public benchmark databases with known Ames assay mutagenicity outcomes for 7300 chemicals (57% mutagens). Of these data, 90% was used to train the model and the remaining 10% was set aside as a holdout set for validation. The model's applicability to drug impurities was tested using a FDA/CDER database of 951 structures, of which 94% were found within the model's applicability domain. The predictive performance of the model is acceptable for supporting regulatory decision-making with 84±1% sensitivity, 81±1% specificity, 83±1% concordance and 79±1% negative predictivity based on internal cross-validation, while the holdout dataset yielded 83% sensitivity, 77% specificity, 80% concordance and 78% negative predictivity. Given the importance of having confidence in negative predictions, an additional external validation of the model was also carried out, using marketed drugs known to be Ames-negative, and obtained 98% coverage and 81% specificity. Additionally, Ames mutagenicity data from FDA/CFSAN was used to create another data set of 1535 chemicals for external validation of the model, yielding 98% coverage, 73% sensitivity, 86% specificity, 81% concordance and 84% negative predictivity.

In silico modeling to predict drug-induced phospholipidosis.

Drug-induced phospholipidosis (DIPL) is a preclinical finding during pharmaceutical drug development that has implications on the course of drug development and regulatory safety review. A principal characteristic of drugs inducing DIPL is known to be a cationic amphiphilic structure. This provides evidence for a structure-based explanation and opportunity to analyze properties and structures of drugs with the histopathologic findings for DIPL. In previous work from the FDA, in silico quantitative structure-activity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the construction and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset. The results of our modeling for DIPL include rigorous external validation tests showing 80-81% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above ≥80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL.

Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities.

Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure-activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describe the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity.

In silico toxicology models and databases as FDA Critical Path Initiative toolkits.

In silico toxicology methods are practical, evidence-based and high throughput, with varying accuracy. In silico approaches are of keen interest, not only to scientists in the private sector and to academic researchers worldwide, but also to the public. They are being increasingly evaluated and applied by regulators. Although there are foreseeable beneficial aspects--including maximising use of prior test data and the potential for minimising animal use for future toxicity testing--the primary use of in silico toxicology methods in the pharmaceutical sciences are as decision support information. It is possible for in silico toxicology methods to complement and strengthen the evidence for certain regulatory review processes, and to enhance risk management by supporting a more informed decision regarding priority setting for additional toxicological testing in research and product development. There are also several challenges with these continually evolving methods which clearly must be considered. This mini-review describes in silico methods that have been researched as Critical Path Initiative toolkits for predicting toxicities early in drug development based on prior knowledge derived from preclinical and clinical data at the US Food and Drug Administration, Center for Drug Evaluation and Research.

Chemoinformatics and chemical genomics: potential utility of in silico methods.

Computational life sciences and informatics are inseparably intertwined and they lie at the heart of modern biology, predictive quantitative modeling and high-performance computing. Two of the applied biological disciplines that are poised to benefit from such progress are pharmacology and toxicology. This review will describe in silico chemoinformatics methods such as (quantitative) structure-activity relationship modeling and will overview how chemoinformatic technologies are considered in applied regulatory research. Given the post-genomics era and large-scale repositories of omics data that are available, this review will also address potential applications of in silico techniques in chemical genomics. Chemical genomics utilizes small molecules to explore the complex biological phenomena that may not be not amenable to straightforward genetic approach. The reader will gain the understanding that chemoinformatics stands at the interface of chemistry and biology with enabling systems for mapping, statistical modeling, pattern recognition, imaging and database tools. The great potential of these technologies to help address complex issues in the toxicological sciences is appreciated with the applied goal of the protection of public health.

Modeling chemical interaction profiles: I. Spectral data-activity relationship and structure-activity relationship models for inhibitors and non-inhibitors of cytochrome P450 CYP3A4 and CYP2D6 isozymes.

An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals--drugs, pesticides, and environmental pollutants--interact at the level of metabolic biotransformations mediated by cytochrome P450 (CYP) enzymes. In the present work, spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches were used to develop machine-learning classifiers of inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference pharmaceutical compounds whose interactions have been deduced from clinical data, and 100 additional chemicals that were used to evaluate model performance in an external validation (EV) test. SDAR is an innovative modeling approach that relies on discriminant analysis applied to binned nuclear magnetic resonance (NMR) spectral descriptors. In the present work, both 1D ¹³C and 1D ¹5N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D ¹³C-NMR and ¹5N-NMR spectra caused an increase in the tenfold cross-validation (CV) performance in terms of both the rate of correct classification and sensitivity. The results of SDAR modeling were verified using SAR. For SAR modeling, a decision forest approach involving from 6 to 17 Mold2 descriptors in a tree was used. Average rates of correct classification of SDAR and SAR models in a hundred CV tests were 60% and 61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4, and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data. Based on unique NMR structural descriptors, the new SDAR modeling method complements the existing SAR techniques, providing an independent estimator that can increase confidence in a structure-activity assessment. When modeling was applied to hazardous environmental chemicals, it was found that up to 20% of them may be substrates and up to 10% of them may be inhibitors of the CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for the environmental health branch of the public health service to extrapolate to hazardous chemicals directly from human clinical data. Therefore, the pharmacological and environmental health branches are both expected to benefit from these reported models.

Computational analysis for hepatic safety signals of constituents present in botanical extracts widely used by women in the United States for treatment of menopausal symptoms.

Black cohosh, red clover, hops, and chasteberry are botanicals commonly used to alleviate menopausal symptoms in the US, and are examined in this study as part of a FDA Office of Women's Health research collaboration to expand knowledge on the safety of these botanical products. Computational approaches using classic (quantitative) structure-activity relationships ((Q)SAR), probabilistic reasoning, machine learning methods, and human expert rule-based systems were employed to deliver human hepatobiliary adverse effect predictions. The objective is to profile and analyze constituents that are alerting for the human hepatobiliary adverse effects. Computational analysis of positively predicted constituents showed that common structural features contributing to the hepatobiliary adverse effect predictions contain phenolic, flavone, isoflavone, glucoside conjugated flavone and isoflavone, and 4-hydroxyacetophenone structures. Specifically, protocatechuic acid from black cohosh, benzofuran and 4-vinylphenol from chasteberry, and xanthohumol I from hops were botanical constituents predicted positive for liver toxicity endpoints and were also confirmed with literature findings. However, comparison between the estimated human exposure to these botanical constituents and the LOAEL and NOAEL in published animal liver toxicology studies for these constituents demonstrated varying margins of safety. This study will serve as regulatory decision support information for regulators at the FDA to help with the process of prioritizing chemicals for testing.

A robotic system for real-time analysis of inhaled submicron and microparticles.

Vitamin E acetate (VEA) has been strongly linked to outbreak of electronic cigarette (EC) or vaping product use-associated lung injury. How VEA leads to such an unexpected morbidity and mortality is currently unknown. To understand whether VEA impacts the disposition profile of inhaled particles, we created a biologically inspired robotic system that quantitatively analyzes submicron and microparticles generated from ECs in real-time while mimicking clinically relevant breathing and vaping topography exactly as happens in humans. We observed addition of even small quantities of VEA was sufficient to alter size distribution and significantly enhance total particles inhaled from ECs. Moreover, we demonstrated utility of our biomimetic robot for studying influence of nicotine and breathing profiles from obstructive and restrictive lung disorders. We anticipate our system will serve as a novel preclinical scientific research, decision-support tool when insight into toxicological impact of modifications in electronic nicotine delivery systems is desired.

Smoke Chemistry, In Vitro Cytotoxicity, and Genotoxicity Demonstrates Enhanced Toxicity of Cigarillos Compared With Cigarettes.

There has been limited toxicity testing of cigarillos, including comparison to cigarettes. This study compared the smoke chemistry and the cytotoxic and genotoxic potential of 10 conventional cigarettes and 10 cigarillos based on the greatest market share. Whole smoke and total particulate matter (TPM) were generated using the Canadian Intense and International Organization for Standardization puffing protocols. Tobacco-specific nitrosamines, carbonyls, and polycyclic aromatic hydrocarbons were measured using gas chromatography-mass spectrometry. TPM smoke extracts were used for the in vitro assays. Cytotoxicity was assessed in human bronchial epithelial continuously cultured cell line cells using the neutral red uptake assay. Genotoxic potential was assessed using the micronucleus (human lung adenocarcinoma continuously cultured cell line cells), Ames, and thymidine kinase assays. TPM from all cigarillos tested was more cytotoxic than cigarettes. Micronucleus formation was significantly greater for cigarillos compared with cigarettes at the highest dose of TPM, with or without rat liver S9 fraction. In the Ames test +S9, both tobacco products exhibited significant dose-dependent increases in mutation frequency, indicating metabolic activation is required for genotoxicity. In the thymidine kinase assay +S9, cigarillos showed a significantly enhanced mutation frequency although both tobacco products were positive. The levels of all measured polycyclic aromatic hydrocarbons, tobacco-specific nitrosamines, and carbonyls (except acrolein) were significantly greater in cigarillos than cigarettes. The Canadian Intense puffing protocol demonstrated increased smoke constituent levels compared with International Organization for Standardization. Even though the gas vapor phase was not tested, the results of this study showed that under the tested conditions the investigated cigarillos showed greater toxicity than comparator cigarettes. This study found that there is significantly greater toxicity in the tested U.S. marketed cigarillos than cigarettes for tobacco constituent levels, cytotoxicity, and genotoxicity. These findings are important for understanding the human health toxicity from the use of cigarillos relative to cigarettes and for building upon knowledge regarding harm from cigarillos to inform risk mitigation strategies.

In silico toxicology for the pharmaceutical sciences.

The applied use of in silico technologies (a.k.a. computational toxicology, in silico toxicology, computer-assisted tox, e-tox, i-drug discovery, predictive ADME, etc.) for predicting preclinical toxicological endpoints, clinical adverse effects, and metabolism of pharmaceutical substances has become of high interest to the scientific community and the public. The increased accessibility of these technologies for scientists and recent regulations permitting their use for chemical risk assessment supports this notion. The scientific community is interested in the appropriate use of such technologies as a tool to enhance product development and safety of pharmaceuticals and other xenobiotics, while ensuring the reliability and accuracy of in silico approaches for the toxicological and pharmacological sciences. For pharmaceutical substances, this means active and impurity chemicals in the drug product may be screened using specialized software and databases designed to cover these substances through a chemical structure-based screening process and algorithm specific to a given software program. A major goal for use of these software programs is to enable industry scientists not only to enhance the discovery process but also to ensure the judicious use of in silico tools to support risk assessments of drug-induced toxicities and in safety evaluations. However, a great amount of applied research is still needed, and there are many limitations with these approaches which are described in this review. Currently, there is a wide range of endpoints available from predictive quantitative structure-activity relationship models driven by many different computational software programs and data sources, and this is only expected to grow. For example, there are models based on non-proprietary and/or proprietary information specific to assessing potential rodent carcinogenicity, in silico screens for ICH genetic toxicity assays, reproductive and developmental toxicity, theoretical prediction of human drug metabolism, mechanisms of action for pharmaceuticals, and newer models for predicting human adverse effects. How accurate are these approaches is both a statistical issue and challenge in toxicology. In this review, fundamental concepts and the current capabilities and limitations of this technology will be critically addressed.

Computational toxicology approaches at the US Food and Drug Administration.

For over a decade, the United States Food and Drug Administration (US FDA) has been engaged in the applied research, development, and evaluation of computational toxicology methods used to support the safety evaluation of a diverse set of regulated products. The basis for evaluating computational toxicology methods is multi-factorial, including the potential for increased efficiency, reduction in the numbers of animals used, lower costs, and the need to explore emerging technologies that support the goals of the US FDA's Critical Path Initiative (e.g. to make decision support information available early in the drug review process). The US FDA's efforts have been facilitated by agency-approved data-sharing agreements between government and commercial software developers. This commentary review describes former and current scientific initiatives at the agency, in the area of computational toxicology methods. In particular, toxicology-based QSAR models, ToxML databases and knowledgebases will be addressed. Notably, many of the computational toxicology tools available are commercial products - however, several are emerging as non-commercial products, which are freely-available to the public, and which will facilitate the understanding of how these programs work and avoid the "black box" paradigm. Through productive collaborations, the US FDA Center for Drug Evaluation and Research, and the Center for Food Safety and Applied Nutrition, have worked together to evaluate, develop and apply these methods to chemical toxicity endpoints of regulatory interest.

In silico toxicological screening of natural products.

ABSTRACT This study closely examines six well-known naturally occurring dietary chemicals (estragole, pulegone, aristolochic acid I, lipoic acid, 1-octacosanol, and epicatechin) with known human exposure, chemical metabolism, and mechanism of action (MOA) using in silico screening methods. The goal of this study was to take into consideration the available information on these chemicals in terms of MOA and experimentally determined toxicological data, and compare them to the in silico predictive modeling results produced from a series of computational toxicology software. After these analyses, a consensus modeling prediction was formulated in light of the weight of evidence for each natural product. We believe this approach of examining the experimentally determined mechanistic data for a given chemical and comparing it to in silico generated predictions and data mining is a valid means to evaluating the utility of the computational software, either alone or in combination with each other. We find that consensus predictions appear to be more accurate than the use of only one or two software programs and our in silico results are in very good agreement with the experimental toxicity data for the natural products screened in this study.