Drug toxicity is frequently caused by electrophilic reactive metabolites that covalently bind to proteins. Epoxides comprise a large class of three-membered cyclic ethers. These molecules are electrophilic and typically highly reactive due to ring tension and polarized carbon–oxygen bonds. Epoxides are metabolites often formed by cytochromes P450 acting on aromatic or double bonds. The specific location on a molecule that undergoes epoxidation is its site of epoxidation (SOE). Identifying a molecule’s SOE can aid in interpreting adverse events related to reactive metabolites and direct modification to prevent epoxidation for safer drugs. This study utilized a database of 702 epoxidation reactions to build a model that accurately predicted sites of epoxidation. The foundation for this model was an algorithm originally designed to model sites of cytochromes P450 metabolism (called XenoSite) that was recently applied to model the intrinsic reactivity of diverse molecules with glutathione. This modeling algorithm systematically and quantitatively summarizes the knowledge from hundreds of epoxidation reactions with a deep convolution network. This network makes predictions at both an atom and molecule level. The final epoxidation model constructed with this approach identified SOEs with 94.9% area under the curve (AUC) performance and separated epoxidized and non-epoxidized molecules with 79.3% AUC. Moreover, within epoxidized molecules, the model separated aromatic or double bond SOEs from all other aromatic or double bonds with AUCs of 92.5% and 95.1%, respectively. Finally, the model separated SOEs from sites of sp² hydroxylation with 83.2% AUC. Our model is the first of its kind and may be useful for the development of safer drugs. The epoxidation model is available at http://swami.wustl.edu/xenosite.

Tetrahydrocannabinol (Δ⁹-THC), the primary psychoactive ingredient in marijuana, is subject to cytochrome P450 oxidation and subsequent UDP-glucuronosyltransferase (UGT)-dependent glucuronidation. Many studies have shown that CYP2C9 and CYP3A4 are the primary enzymes responsible for these cytochrome P450-dependent oxidations, but little work has been done to characterize phase II metabolic pathways. In this study, we test the hypothesis that there are specific human UGTs responsible for classic cannabinoid metabolism. The activities of 12 human recombinant UGTs toward classic cannabinoids [cannabinol (CBN), cannabidiol (CBD), (–)-Δ⁸-THC, (–)-Δ⁹-THC, (±)-11-hydroxy-Δ⁹-THC (THC-OH), and (–)-11-nor-9-carboxy-Δ⁹-THC (THC-COOH)] were evaluated using high-performance liquid chromatography-tandem mass spectrometry and labeling assays. Despite activity by UGT1A1, 1A3, 1A8, 1A9, 1A10, and 2B7 toward CBN, CBD, THC-OH, and THC-COOH, only selected UGTs demonstrate sufficient activity for further characterization of steady-state kinetics. CBN was the most recognized substrate as evidenced by activities from hepatic UGT1A9 and extrahepatic UGT1A7, UGT1A8, and UGT1A10. These results may reflect the introduction of an aromatic ring to Δ⁹-THC, leading to favorable π stacking with phenylalanines in the UGT active site. Likewise, oxidation of Δ⁹-THC to THC-OH results in UGT1A9 and UGT1A10 activity toward the cannabinoid. Further oxidation to THC-COOH surprisingly leads to a loss in metabolism by UGT1A9 and UGT1A10, while creating a substrate recognized by UGT1A1 and UGT1A3. The resulting glucuronide of THC-COOH is the main metabolite found in urine, and thus these hepatic enzymes play a critical role in the metabolic clearance of cannabinoids. Taken together, glucuronidation of cannabinoids depends on upstream processing including enzymes such as CYP2C9 and CYP3A4.

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.

In this study we offer a mechanistic interpretation of the previously known but unexplained substrate inhibition observed for CYP2E1. At low substrate concentrations, p-nitrophenol (pNP) was rapidly turned over (47 min^-1) with relatively low K_m (24 μm); nevertheless, at concentrations of >100 μm, the rate of pNP oxidation gradually decreased as a second molecule bound to CYP2E1 through an effector site (K_ss = 260 μm), which inhibited activity at the catalytic site. 4-Methylpyrazole (4MP) was a potent inhibitor for both sites through a mixed inhibition mechanism. The K_i for the catalytic site was 2.0 μm. Although we were unable to discriminate whether an EIS or ESI complex formed, the respective inhibition constants were far lower than K_ss. Bicyclic indazole (IND) inhibited catalysis through a single CYP2E1 site (K_i = 0.12 μm). Similarly, 4MP and IND yielded type II binding spectra that reflected the association of either two 4MP or one IND molecule(s) to CYP2E1, respectively. Based on computational docking studies with a homology model for CYP2E1, the two sites for monocyclic molecules, pNP and 4MP, exist within a narrow channel connecting the active site to the surface of the enzyme. Because of the presence of the heme iron, one site supports catalysis, whereas the other more distal effector site binds molecules that can influence the binding orientation and egress of molecules for the catalytic site. Although IND did not bind these sites simultaneously, the presence of IND at the catalytic site blocked binding at the effector site.