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 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.

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.

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 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.

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.

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.

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.

Safety evaluation of substituted thiophenes used as flavoring ingredients.

This publication is the second in a series by the Expert Panel of the Flavor and Extract Manufacturers Association summarizing the conclusions of its third systematic re-evaluation of the safety of flavorings previously considered to be generally recognized as safe (GRAS) under conditions of intended use. Re-evaluation of GRAS status for flavorings is based on updated considerations of exposure, structural analogy, metabolism, pharmacokinetics and toxicology and includes a comprehensive review of the scientific information on the flavorings and structurally related substances. Of the 12 substituted thiophenes reviewed here, 11 were reaffirmed as GRAS based on their rapid absorption, metabolism and excretion in humans and animals; the low estimated dietary exposure from flavor use; the wide margins of safety between the conservative estimates of intake and the no-observed-adverse effect levels; and the lack of significant genotoxic and mutagenic potential. For one of the substituted thiophenes, 3-acetyl-2,5-dimethylthiophene, it was concluded that more detailed exposure information, comparative metabolism studies and comprehensive toxicity data, including an in-depth evaluation of the mechanism of action for any adverse effects observed, are required for continuation of its FEMA GRAS™ status. In the absence of these data, the compound was removed from the FEMA GRAS list.

Safety and performance benefits of arginine supplements for military personnel: a systematic review.

CONTEXT: Dietary supplements are widely used by military personnel and civilians for promotion of health. OBJECTIVE: The objective of this evidence-based review was to examine whether supplementation with l-arginine, in combination with caffeine and/or creatine, is safe and whether it enhances athletic performance or improves recovery from exhaustion for military personnel. DATA SOURCES: Information from clinical trials and adverse event reports were collected from 17 databases and 5 adverse event report portals. STUDY SELECTION: Studies and reports were included if they evaluated the safety and the putative outcomes of enhanced performance or improved recovery from exhaustion associated with the intake of arginine alone or in combination with caffeine and/or creatine in healthy adults aged 19 to 50 years. DATA EXTRACTION: Information related to population, intervention, comparator, and outcomes was abstracted. Of the 2687 articles screened, 62 articles meeting the inclusion criteria were analyzed. Strength of evidence was assessed in terms of risk of bias, consistency, directness, and precision. RESULTS: Most studies had few participants and suggested risk of bias that could negatively affect the results. l-Arginine supplementation provided little enhancement of athletic performance or improvements in recovery. Short-term supplementation with arginine may result in adverse gastrointestinal and cardiovascular effects. No information about the effects of arginine on the performance of military personnel was available. CONCLUSIONS: The available information does not support the use of l-arginine, either alone or in combination with caffeine, creatine, or both, to enhance athletic performance or improve recovery from exhaustion. Given the information gaps, an evidence-based review to assess the safety or effectiveness of multi-ingredient dietary supplements was not feasible, and therefore the development of a computational model-based approach to predict the safety of multi-ingredient dietary supplements is recommended.

Utility of models of the gastrointestinal tract for assessment of the digestion and absorption of engineered nanomaterials released from food matrices.

Engineered metal/mineral, lipid and biochemical macromolecule nanomaterials (NMs) have potential applications in food. Methodologies for the assessment of NM digestion and bioavailability in the gastrointestinal tract are nascent and require refinement. A working group was tasked by the International Life Sciences Institute NanoRelease Food Additive project to review existing models of the gastrointestinal tract in health and disease, and the utility of these models for the assessment of the uptake of NMs intended for food. Gastrointestinal digestion and absorption could be addressed in a tiered approach using in silico computational models, in vitro non-cellular fluid systems and in vitro cell culture models, after which the necessity of ex vivo organ culture and in vivo animal studies can be considered. Examples of NM quantification in gastrointestinal tract fluids and tissues are emerging; however, few standardized analytical techniques are available. Coupling of these techniques to gastrointestinal models, along with further standardization, will further strengthen methodologies for risk assessment.

Evidence-based evaluation of potential benefits and safety of beta-alanine supplementation for military personnel.

This Department of Defense-sponsored evidence-based review evaluates the safety and putative outcomes of enhancement of athletic performance or improved recovery from exhaustion in studies involving beta-alanine alone or in combination with other ingredients. Beta-alanine intervention studies and review articles were collected from 13 databases, and safety information was collected from adverse event reporting portals. Due to the lack of systematic studies involving military populations, all the available literature was assessed with a subgroup analysis of studies on athletes to determine if beta-alanine would be suitable for the military. Available literature provided only limited evidence concerning the benefits of beta-alanine use, and a majority of the studies were not designed to address safety. Overall, the strength of evidence in terms of the potential for risk of bias in the quality of the available literature, consistency, directness, and precision did not support the use of beta-alanine by military personnel. The strength of evidence for a causal relation between beta-alanine and paresthesia was moderate.

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.

Development of cardiac safety translational tools for QT prolongation and torsade de pointes.

OBJECTIVE: A regulatory science priority at the Food and Drug Administration (FDA) is to promote the development of new innovative tools such as reliable and validated computational (in silico) models. This FDA Critical Path Initiative project involved the development of predictive clinical computational models for decision-support in CDER evaluations of QT/QTc interval prolongation and proarrhythmic potential for non-antiarrhythmic drugs. METHODS: Several classification models were built using predictive technologies of quantitative structure-activity relationship analysis using clinical in-house and public data on induction of QT prolongation and torsade de pointes (TdP) in humans. Specific models were geared toward prediction of high-risk drugs with attention to outcomes from thorough QT studies and TdP risk based on clinical in-house data. Models used were independent of non-clinical data or known molecular mechanisms. The positive predictive performance of the in silico models was validated using cross-validation and independent external validation test sets. RESULTS: Optimal performance was observed with high sensitivity (87%) and high specificity (88%) for predicting QT interval prolongation using in-house data, and 77% sensitivity in predicting drugs withdrawn from the market. Furthermore, the article describes alerting substructural features based on drugs tested in the clinical trials. CONCLUSIONS: The in silico models provide evidence of a structure-based explanation for these cardiac safety endpoints. The models will be made publically available and are under continual prospective external validation testing and updating at CDER using TQT study outcomes.

Predictive computational toxicology to support drug safety assessment.

Use of predictive technologies is an important aspect of many efforts in today's research, development, and regulatory landscapes. Computational methods as predictive tools for supporting drug safety assessments is of widespread interest as the field of in silico assessments rapidly changes with emerging technologies and the large amount of existing data available for modeling. There are challenges associated with application of in silico analyses for drug toxicity predictions and need for strategies and harmonization to enable an acceptable in silico evaluation for prediction of specific toxicity assay outcomes. This chapter will provide an overview focused on computational tools using structure-activity relationships and will highlight initiatives for use of computational assessments and realistic applications for predictive modeling in evaluating potential toxicities of drug-related molecules.

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.