Determination of Lucidin-Specific DNA Adducts by Liquid Chromatography with Tandem Mass Spectrometry in the Livers and Kidneys of Rats Given Lucidin-3-O-primeveroside

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.

Synthesis of N2 -Aryl-2'-Deoxyguanosine Modified Phosphoramidites and Oligonucleotides

The N² -position of 2'-deoxyguanosine (N² -position in dG) is well known for forming carcinogenic minor groove DNA adducts, which originate from environmental pollutants, chemicals, and tobacco smoke. The N² -dG DNA adducts have strong implications on biological processes such as DNA replication and repair and may, therefore, result in genomic instability by generating mutations or even cell death. It is crucial to know the role of DNA polymerases when they encounter the N² -dG damaged site in DNA. To get detailed insights on the in vitro DNA damage tolerance or bypass mechanism, there is a need to synthetically access N² -dG damaged DNAs. This article describes a detailed protocol of the synthesis of N² -aryl-dG modified nucleotides using the Buchwald-Hartwig reaction as a main step and incorporation of the modified nucleotides into DNA. In Basic Protocol 1, we focused on the synthesis of five different N² -dG modified phosphoramidites with varying bulkiness (benzyl to pyrenyl). Basic Protocol 2 describes the details of synthesizing N² -dG modified oligonucleotides employing the standard solid phase synthesis protocol. This strategy provides robust synthetic access to various modifications at the N² -position of dG; the modified dGs serve as good substrates to study translesion synthesis and repair pathways. Overall data presented in this article are based on earlier published reports. © 2019 by John Wiley & Sons, Inc.

Effects of inhibition of hepatic sulfotransferase activity on renal genotoxicity induced by lucidin-3-O-primeveroside

Sulfotransferase 1A (SULT1A) expression is lower in the liver of humans than that of rodents. Therefore, species differences should be taken into consideration when assessing the risk of rodent hepatocarcinogens metabolically activated by SULT1A in humans. Although some renal carcinogens require SULT1A-mediated activation, it is unclear how SULT1A activity in the liver affects renal carcinogens. To explore the effects of SULT1A activity in the liver on genotoxicity induced by SULT1A-activated renal carcinogens, B6C3F₁ mice or gpt delta mice of the same strain background were given lucidin-3-O-primeveroside (LuP), a hepatic and renal carcinogen of rodents, for 4 or 13 weeks, respectively, and pentachlorophenol (PCP) as a liver-specific SULT inhibitor, was given from 1 week before LuP treatment to the end of the experiment. A 4 week exposure of LuP induced lucidin-specific DNA adduct formation. The suppression of Sult1a expression was observed only in the liver but not in the kidneys of PCP-treated mice, but co-administration of PCP suppressed LuP-induced DNA adduct formation in both organs. Thirteen-week exposure of LuP increased mutation frequencies and cotreatment with PCP suppressed these increases in both organs. Given that intact levels of SULT activity in the liver were much higher than in the kidneys of rodents, SULT1A may predominantly activate LuP in the liver, consequently leading to genotoxicity not only in the liver but also in the kidney. Thus, species differences should be considered in human risk assessment of renal carcinogens activated by SULT1A as in the case of the corresponding liver carcinogens.

Mechanism of Error-Free DNA Replication Past Lucidin-Derived DNA Damage by Human DNA Polymerase κ

DNA damage impinges on genetic information flow and has significant implications in human disease and aging. Lucidin-3-O-primeveroside (LuP) is an anthraquinone derivative present in madder root, which has been used as a coloring agent and food additive. LuP can be metabolically converted to genotoxic compound lucidin, which subsequently forms lucidin-specific N²-2'-deoxyguanosine (N²-dG) and N⁶-2'-deoxyadenosine (N⁶-dA) DNA adducts. Lucidin is mutagenic and carcinogenic in rodents but has low carcinogenic risks in humans. To understand the molecular mechanism of low carcinogenicity of lucidin in humans, we performed DNA replication assays using site-specifically modified oligodeoxynucleotides containing a structural analogue (LdG) of lucidin-N²-dG DNA adduct and determined the crystal structures of DNA polymerase (pol) κ in complex with LdG-bearing DNA and an incoming nucleotide. We examined four human pols (pol η, pol ι, pol κ, and Rev1) in their efficiency and accuracy during DNA replication with LdG; these pols are key players in translesion DNA synthesis. Our results demonstrate that pol κ efficiently and accurately replicates past the LdG adduct, whereas DNA replication by pol η, pol ι is compromised to different extents. Rev1 retains its ability to incorporate dCTP opposite the lesion albeit with decreased efficiency. Two ternary crystal structures of pol κ illustrate that the LdG adduct is accommodated by pol κ at the enzyme active site during insertion and postlesion-extension steps. The unique open active site of pol κ allows the adducted DNA to adopt a standard B-form for accurate DNA replication. Collectively, these biochemical and structural data provide mechanistic insights into the low carcinogenic risk of lucidin in humans.

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.