Predicted Contributions of Flavin-containing Monooxygenases to the N-oxygenation of Drug Candidates Based on their Estimated Base Dissociation Constants

AIMS

Base dissociation constants of 30 model chemicals were investigated to constitute potential determinant factors predicting the contributions of flavin-containing monooxygenases (FMOs).

BACKGROUND

The contributions of FMOs to the metabolic elimination of new drug candidates could be underestimated under certain experimental conditions during drug development.

OBJECTIVE

A method for predicting metabolic sites and the contributions of FMOs to N-oxygenations is proposed using a molecular descriptor, the base dissociation constant (pKa base), which can be estimated in silico using commonly available chemoinformatic prediction systems.

METHODS

Model drugs and their oxidative pathways were surveyed in the literature to investigate the roles of FMOs in their N-oxygenations. The acid and base dissociation constants of the nitrogen moieties of 30 model substrates were estimated using well-established chemoinformatic software.

RESULTS

The base dissociation constants of 30 model chemicals were classified into two groups based on the reported optimal in vitro pH of 8.4 for FMO enzymes as a key determinant factor. Among 18 substrates (e.g., trimethylamine, benzydamine, and itopride) with pKa (base) values in the range of 8.4-9.8, all N-oxygenated metabolites were reported to be predominantly catalyzed by FMOs. Except for three cases (xanomeline; L-775,606; and tozasertib), the nine substrates with pKa (base) values in the range 2.7-7.9 were only moderately or minorly N-oxygenated by FMOs in addition to their major metabolic pathway of oxidation mediated by cytochrome P450s. N-Oxygenation of T-1032 (with a pKa of 4.8) is mediated predominantly by P450 3A5, but not by FMO1/3.

CONCLUSION

The predicted contributions of FMOs to the N-oxygenation of drug candidates can be simply estimated using classic base dissociation constants.

The UDP-Glycosyltransferase (UGT) Superfamily: New Members, New Functions, and Novel Paradigms

UDP-glycosyltransferases (UGTs) catalyze the covalent addition of sugars to a broad range of lipophilic molecules. This biotransformation plays a critical role in elimination of a broad range of exogenous chemicals and by-products of endogenous metabolism, and also controls the levels and distribution of many endogenous signaling molecules. In mammals, the superfamily comprises four families: UGT1, UGT2, UGT3, and UGT8. UGT1 and UGT2 enzymes have important roles in pharmacology and toxicology including contributing to interindividual differences in drug disposition as well as to cancer risk. These UGTs are highly expressed in organs of detoxification (e.g., liver, kidney, intestine) and can be induced by pathways that sense demand for detoxification and for modulation of endobiotic signaling molecules. The functions of the UGT3 and UGT8 family enzymes have only been characterized relatively recently; these enzymes show different UDP-sugar preferences to that of UGT1 and UGT2 enzymes, and to date, their contributions to drug metabolism appear to be relatively minor. This review summarizes and provides critical analysis of the current state of research into all four families of UGT enzymes. Key areas discussed include the roles of UGTs in drug metabolism, cancer risk, and regulation of signaling, as well as the transcriptional and posttranscriptional control of UGT expression and function. The latter part of this review provides an in-depth analysis of the known and predicted functions of UGT3 and UGT8 enzymes, focused on their likely roles in modulation of levels of endogenous signaling pathways.

Predicting the Metabolic Sites by Flavin-Containing Monooxygenase on Drug Molecules Using SVM Classification on Computed Quantum Mechanics and Circular Fingerprints Molecular Descriptors

As an important enzyme in Phase I drug metabolism, the flavin-containing monooxygenase (FMO) also metabolizes some xenobiotics with soft nucleophiles. The site of metabolism (SOM) on a molecule is the site where the metabolic reaction is exerted by an enzyme. Accurate prediction of SOMs on drug molecules will assist the search for drug leads during the optimization process. Here, some quantum mechanics features such as the condensed Fukui function and attributes from circular fingerprints (called Molprint2D) are computed and classified using the support vector machine (SVM) for predicting some potential SOMs on a series of drugs that can be metabolized by FMO enzymes. The condensed Fukui function fA- representing the nucleophilicity of central atom A and the attributes from circular fingerprints accounting the influence of neighbors on the central atom. The total number of FMO substrates and non-substrates collected in the study is 85 and they are equally divided into the training and test sets with each carrying roughly the same number of potential SOMs. However, only N-oxidation and S-oxidation features were considered in the prediction since the available C-oxidation data was scarce. In the training process, the LibSVM package of WEKA package and the option of 10-fold cross validation are employed. The prediction performance on the test set evaluated by accuracy, Matthews correlation coefficient and area under ROC curve computed are 0.829, 0.659, and 0.877 respectively. This work reveals that the SVM model built can accurately predict the potential SOMs for drug molecules that are metabolizable by the FMO enzymes.

Transcriptional regulation of human UDP-glucuronosyltransferase genes

Glucuronidation is an important metabolic pathway for many small endogenous and exogenous lipophilic compounds, including bilirubin, steroid hormones, bile acids, carcinogens and therapeutic drugs. Glucuronidation is primarily catalyzed by the UDP-glucuronosyltransferase (UGT) 1A and two subfamilies, including nine functional UGT1A enzymes (1A1, 1A3-1A10) and 10 functional UGT2 enzymes (2A1, 2A2, 2A3, 2B4, 2B7, 2B10, 2B11, 2B15, 2B17 and 2B28). Most UGTs are expressed in the liver and this expression relates to the major role of hepatic glucuronidation in systemic clearance of toxic lipophilic compounds. Hepatic glucuronidation activity protects the body from chemical insults and governs the therapeutic efficacy of drugs that are inactivated by UGTs. UGT mRNAs have also been detected in over 20 extrahepatic tissues with a unique complement of UGT mRNAs seen in almost every tissue. This extrahepatic glucuronidation activity helps to maintain homeostasis and hence regulates biological activity of endogenous molecules that are primarily inactivated by UGTs. Deciphering the molecular mechanisms underlying tissue-specific UGT expression has been the subject of a large number of studies over the last two decades. These studies have shown that the constitutive and inducible expression of UGTs is primarily regulated by tissue-specific and ligand-activated transcription factors (TFs) via their binding to cis-regulatory elements (CREs) in UGT promoters and enhancers. This review first briefly summarizes published UGT gene transcriptional studies and the experimental models and tools utilized in these studies, and then describes in detail the TFs and their respective CREs that have been identified in the promoters and/or enhancers of individual UGT genes.

Flavin monooxygenase metabolism: why medicinal chemists should matter

FMO enzymes (FMOs) play a key role in the processes of detoxification and/or bioactivation of specific pharmaceuticals and xenobiotics bearing nucleophilic centers. The N-oxide and S-oxide metabolites produced by FMOs are often active metabolites. The FMOs are more active than cytochromes in the brain and work in tandem with CYP3A4 in the liver. FMOs might reduce the risk of phospholipidosis of CAD-like drugs, although some FMOs metabolites seem to be neurotoxic and hepatotoxic. However, in silico methods for FMO metabolism prediction are not yet available. This paper reports, for the first time, a substrate-specificity and catalytic-activity model for FMO3, the most relevant isoform of the FMOs in humans. The application of this model to a series of compounds with unknown FMO metabolism is also reported. The model has also been very useful to design compounds with optimal clearance and in finding erroneous literature data, particularly cases in which substances have been reported to be FMO3 substrates when, in reality, the experimentally validated in silico model correctly predicts that they are not.