Epoxyalcohols: bioactivation and conjugation required for skin sensitization

Quantitative determination of urinary metabolites of geraniol by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS)

Geraniol is a fragrance which occurs in natural terpene oil or is chemically synthesized on a large scale. It is used in a wide variety of consumer products such as perfumes, deodorants, household products and cosmetics. Hence, not only industry workers in the production of geraniol, but also consumers can come into contact with the substance. Human biomonitoring (HBM), i.e. the analytical determination of substances and their metabolites in human biological material, is a key element in the analysis and assessment of the distribution and intensity of occupational and environmental exposure of humans. Therefore, a procedure for the quantitative determination of the urinary metabolites Hildebrandt acid, geranic acid, 3-hydroxycitronellic acid and 8-carboxygeraniol as potential biomarkers of geraniol exposure was developed and validated. The method is based on ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) after enzymatic hydrolysis and liquid-liquid extraction (LLE) of the target analytes. The limit of quantification (LOQ) is 1.5 μg L^-1 for 8-carboxygeraniol, 2.7 μg L^-1 each for Hildebrandt acid and geranic acid, and 1.8 μg L^-1 for 3-hydroxycitronellic acid. The method was applied to urine samples of 41 persons without occupational exposure to geraniol. Hildebrandt acid and geranic acid were detected in all samples, 8-carboxygeraniol in 83% and 3-hydroxycitronellic acid in 81% of the samples. Hildebrandt acid (median: 313 μg L^-1, range: 37-1966 μg L^-1) was the most abundant metabolite, followed by geranic acid (93 μg L^-1; 9-477 μg L^-1), 3-hydroxycitronellic acid (18 μg L^-1; <LOQ to 70 μg L^-1) and 8-carboxygeraniol (9 μg L^-1; <LOQ to 46 μg L^-1). Hildebrandt acid, geranic acid and 3-hydroxycitronellic acid apparently represent larger relative fractions of the eliminated metabolites, but they are not strictly specific for geraniol since they are metabolites of other terpenes as well, such as citral. In contrast, geraniol seems to be the only parent compound for 8-carboxygeraniol, which makes this metabolite a promising candidate for specific human biomonitoring and risk assessment.

Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites

Adverse drug metabolism often severely impacts patient morbidity and mortality. Unfortunately, drug metabolism experimental assays are costly, inefficient, and slow. Instead, computational modeling could rapidly flag potentially toxic molecules across thousands of candidates in the early stages of drug development. Most metabolism models focus on predicting sites of metabolism (SOMs): the specific substrate atoms targeted by metabolic enzymes. However, SOMs are merely a proxy for metabolic structures: knowledge of an SOM does not explicitly provide the actual metabolite structure. Without an explicit metabolite structure, computational systems cannot evaluate the new molecule's properties. For example, the metabolite's reactivity cannot be automatically predicted, a crucial limitation because reactive drug metabolites are a key driver of adverse drug reactions (ADRs). Additionally, further metabolic events cannot be forecast, even though the metabolic path of the majority of substrates includes two or more sequential steps. To overcome the myopia of the SOM paradigm, this study constructs a well-defined system-termed the metabolic forest-for generating exact metabolite structures. We validate the metabolic forest with the substrate and product structures from a large, chemically diverse, literature-derived dataset of 20 736 records. The metabolic forest finds a pathway linking each substrate and product for 79.42% of these records. By performing a breadth-first search of depth two or three, we improve performance to 88.43 and 88.77%, respectively. The metabolic forest includes a specialized algorithm for producing accurate quinone structures, the most common type of reactive metabolite. To our knowledge, this quinone structure algorithm is the first of its kind, as the diverse mechanisms of quinone formation are difficult to systematically reproduce. We validate the metabolic forest on a previously published dataset of 576 quinone reactions, predicting their structures with a depth three performance of 91.84%. The metabolic forest accurately enumerates metabolite structures, enabling promising new directions such as joint metabolism and reactivity modeling.

Patch testing with purified and oxidized citronellol

BACKGROUND

Citronellol is a commonly used fragrance terpene included in fragrance mix II. As with many other fragrance terpenes, citronellol is susceptible to autoxidation. Citronellol hydroperoxides are formed in large amounts and are the only oxidation products identified as sensitizers in oxidized citronellol.

AIM

To compare frequencies of contact allergy to purified and oxidized citronellol and to investigate the pattern of concomitant reactions to fragrance markers of the baseline series, oxidized linalool, and oxidized limonene.

METHODS

A total of 658 dermatitis patients were patch tested with purified and oxidized citronellol at 2.0%, 4.0%, 6.0%, and 1.0%, 2.0%, 4.0%, 6.0% petrolatum, respectively. The irritant properties of purified and oxidized citronellol were studied before patch testing.

RESULTS

Few irritant reactions were observed in the pretest. Purified citronellol detected positive reactions in 0.15%-0.31% of patients, while oxidized citronellol detected positive reactions in 0.61%-4.5%. Among patients reacting to oxidized citronellol, 34%-50% showed concomitant reactions to fragrance markers of the baseline series and 75%-91% to oxidized linalool or oxidized limonene.

CONCLUSION

Oxidized citronellol detects more cases of contact allergy than purified citronellol, and these cases are not all detected using fragrance mix II. Patch testing with oxidized citronellol will add to the tools in the diagnosis of fragrance allergy.

Using Transition State Modeling To Predict Mutagenicity for Michael Acceptors

The Ames mutagenicity assay is a long established in vitro test to measure the mutagenicity potential of a new chemical used in regulatory testing globally. One of the key computational approaches to modeling of the Ames assay relies on the formation of chemical categories based on the different electrophilic compounds that are able to react directly with DNA and form a covalent bond. Such approaches sometimes predict false positives, as not all Michael acceptors are found to be Ames-positive. The formation of such covalent bonds can be explored computationally using density functional theory transition state modeling. We have applied this approach to mutagenicity, allowing us to calculate the activation energy required for α,β-unsaturated carbonyls to react with a model system for the guanine nucleobase of DNA. These calculations have allowed us to identify that chemical compounds with activation energies greater than or equal to 25.7 kcal/mol are not able to bind directly to DNA. This allows us to reduce the false positive rate for computationally predicted mutagenicity assays. This methodology can be used to investigate other covalent-bond-forming reactions that can lead to toxicological outcomes and learn more about experimental results.

What Happens after Activation of Ascaridole? Reactive Compounds and Their Implications for Skin Sensitization

To replace animal testing and improve the prediction of skin sensitization, significant attention has been directed to the use of alternative methods. The direct peptide reactivity assay (DPRA), the regulatory agencies' approved alternative in chemico method, has been applied for understanding the sensitization capacity of activated ascaridole. Ascaridole, the oxidative metabolite of α-terpinene, is considered to be one of the components responsible for the contact allergy associated with essential oils derived from Chenopodium and Melaleuca species. The recently developed high-throughput screening based on the dansyl cysteamine (HTS-DCYA) method was applied to understand the reported enhanced reactivity of activated ascaridole and possibly to identify the resulting elusive radical or other reactive species. For the first time, a substituted cyclohexenone was identified as a potential electrophilic intermediate resulting in higher depletion of nucleophilic DCYA, along with several nonreactive byproducts of ascaridole via a radical degradation mechanism. Formation of electrophilic species via radical degradation is one of the possible pathways should be considered for the peptide reactivity of in aged tea tree oil or oils rich in terpinenes along with commonly believed reactants, allylic-epoxides and allylic-peroxides.

Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network

Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural network-the XenoSite reactivity model-using literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the model's performances significantly outperformed reactivity indices derived from quantum simulations that are reported in the literature. Moreover, we developed and applied a selectivity score to assess preferential reactions with the macromolecules as opposed to the common screening traps. For the entire data set of 2803 molecules, this approach yielded totals of 257 (9.2%) and 227 (8.1%) molecules predicted to be reactive only with DNA and protein, respectively, and hence those that would be missed by standard reactivity screening experiments. Site of reactivity data is an underutilized resource that can be used to not only predict if molecules are reactive, but also show where they might be modified to reduce toxicity while retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity.