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.

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.

Testing computational toxicology models with phytochemicals.

Computational toxicology employing quantitative structure-activity relationship (QSAR) modeling is an evidence-based predictive method being evaluated by regulatory agencies for risk assessment and scientific decision support for toxicological endpoints of interest such as rodent carcinogenicity. Computational toxicology is being tested for its usefulness to support the safety assessment of drug-related substances (e.g. active pharmaceutical ingredients, metabolites, impurities), indirect food additives, and other applied uses of value for protecting public health including safety assessment of environmental chemicals. The specific use of QSAR as a chemoinformatic tool for estimating the rodent carcinogenic potential of phytochemicals present in botanicals, herbs, and natural dietary sources is investigated here by an external validation study, which is the most stringent scientific method of measuring predictive performance. The external validation statistics for predicting rodent carcinogenicity of 43 phytochemicals, using two computational software programs evaluated at the FDA, are discussed. One software program showed very good performance for predicting non-carcinogens (high specificity), but both exhibited poor performance in predicting carcinogens (sensitivity), which is consistent with the design of the models. When predictions were considered in combination with each other rather than based on any one software, the performance for sensitivity was enhanced, However, Chi-square values indicated that the overall predictive performance decreases when using the two computational programs with this particular data set. This study suggests that complementary multiple computational toxicology software need to be carefully selected to improve global QSAR predictions for this complex toxicological endpoint.

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.

Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling.

Consistent with the U.S. Food and Drug Administration (FDA) Critical Path Initiative, predictive toxicology software programs employing quantitative structure-activity relationship (QSAR) models are currently under evaluation for regulatory risk assessment and scientific decision support for highly sensitive endpoints such as carcinogenicity, mutagenicity and reproductive toxicity. At the FDA's Center for Food Safety and Applied Nutrition's Office of Food Additive Safety and the Center for Drug Evaluation and Research's Informatics and Computational Safety Analysis Staff (ICSAS), the use of computational SAR tools for both qualitative and quantitative risk assessment applications are being developed and evaluated. One tool of current interest is MDL-QSAR predictive discriminant analysis modeling of rodent carcinogenicity, which has been previously evaluated for pharmaceutical applications by the FDA ICSAS. The study described in this paper aims to evaluate the utility of this software to estimate the carcinogenic potential of small, organic, naturally occurring chemicals found in the human diet. In addition, a group of 19 known synthetic dietary constituents that were positive in rodent carcinogenicity studies served as a control group. In the test group of naturally occurring chemicals, 101 were found to be suitable for predictive modeling using this software's discriminant analysis modeling approach. Predictions performed on these compounds were compared to published experimental evidence of each compound's carcinogenic potential. Experimental evidence included relevant toxicological studies such as rodent cancer bioassays, rodent anti-carcinogenicity studies, genotoxic studies, and the presence of chemical structural alerts. Statistical indices of predictive performance were calculated to assess the utility of the predictive modeling method. Results revealed good predictive performance using this software's rodent carcinogenicity module of over 1200 chemicals, comprised primarily of pharmaceutical, industrial and some natural products developed under an FDA-MDL cooperative research and development agreement (CRADA). The predictive performance for this group of dietary natural products and the control group was 97% sensitivity and 80% concordance. Specificity was marginal at 53%. This study finds that the in silico QSAR analysis employing this software's rodent carcinogenicity database is capable of identifying the rodent carcinogenic potential of naturally occurring organic molecules found in the human diet with a high degree of sensitivity. It is the first study to demonstrate successful QSAR predictive modeling of naturally occurring carcinogens found in the human diet using an external validation test. Further test validation of this software and expansion of the training data set for dietary chemicals will help to support the future use of such QSAR methods for screening and prioritizing the risk of dietary chemicals when actual animal data are inadequate, equivocal, or absent.

Mammalian iron metabolism.

ABSTRACT Iron is an essential transition metal for mammalian cellular and tissue viability. It is critical to supplying oxygen through heme, the mitochondrial respiratory chain, and enzymes such as ribonucleotide reductase. Mammalian organisms have evolved with the means of regulating the metabolism of iron, because if left unregulated, the resulting excess amounts of iron may induce chronic toxicities affecting multiple organ systems. Several homeostatic mechanisms exist to control the amount of intestinal dietary iron uptake, cellular iron uptake, distribution, and export. Within these processes, numerous molecular participants have been identified because of advancements in basic cell biology and efforts in disease-based research of iron storage abnormalities. For example, dietary iron uptake across the intestinal duodenal mucosa is mediated by an intramembrane divalent metal transporter 1 (DMT1), and cellular iron efflux involves ferroportin, the only known iron exporter. In addition to duodenal enterocytes, ferroportin is present in other cell types, and exports iron into plasma. Ferroportin was recently discovered to be regulated by the expression of the circulating hormone hepcidin, a small peptide synthesized in hepatocytes. These recent studies on the role of hepcidin in the regulation of dietary, cellular, and extracellular iron have led to a better understanding of the pathways by which iron balance in humans is influenced, especially its involvement in human genetic diseases of iron overload. Other important molecular pathways include iron binding to transferrin in the bloodstream for cellular delivery through the plasma membrane transferrin receptor (TfR1). In the cytosol, iron regulatory proteins 1 and 2 (IRP1 and IRP2) play a prominent role in sensing the presence of iron in order to posttranscriptionally regulate the expression of TfR1 and ferritin, two important participants in iron metabolism. From a toxicological standpoint, posttranscriptional regulation of these genes aids in the sequestration, control, and hence prevention of cytotoxic effects from free-floating nontransferrin-bound iron. Given the importance of dietary iron in normal physiology, its potential to induce chronic toxicity, and recent discoveries in the regulation of human iron metabolism by hepcidin, this review will address the regulatory mechanisms of normal iron metabolism in mammals with emphasis on dietary exposure. It is the goal of this review that this information may provide in a concise format our current understanding of major pathways and mechanisms involved in mammalian iron metabolism, which is a basis for control of iron toxicity. Such a discussion is intended to facilitate the identification of deficiencies so that future metabolic or toxicological studies may be appropriately focused. A better knowledge of iron metabolism from normal to pathophysiological conditions will ultimately broaden the spectrum of the usefulness of this information in biomedical and toxicological sciences for improving and protecting human health.

Medicinal plants from Peru: a review of plants as potential agents against cancer.

Natural products have played a significant role in drug discovery and development especially for agents against cancer and infectious disease. An analysis of new and approved drugs for cancer by the United States Food and Drug Administration over the period of 1981-2002 showed that 62% of these cancer drugs were of natural origin. Natural compounds possess highly diverse and complex molecular structures compared to small molecule synthetic drugs and often provide highly specific biological activities likely derived from the rigidity and high number of chiral centers. Ethnotraditional use of plant-derived natural products has been a major source for discovery of potential medicinal agents. A number of native Andean and Amazonian medicines of plant origin are used as traditional medicine in Peru to treat different diseases. Of particular interest in this mini-review are three plant materials endemic to Peru with the common names of Cat's claw (Uncaria tomentosa), Maca (Lepidium meyenii), and Dragon's blood (Croton lechleri) each having been scientifically investigated for a wide range of therapeutic uses including as specific anti-cancer agents as originally discovered from the long history of traditional usage and anecdotal information by local population groups in South America. Against this background, we present an evidence-based analysis of the chemistry, biological properties, and anti-tumor activities for these three plant materials. In addition, this review will discuss areas requiring future study and the inherent limitations in their experimental use as anti-cancer agents.